Datasets:

zurlog

/

subset_wlabels

like 0

2012.05525

Yes

2020-12-10

Preprint

arXiv

detection of covid-19 patients with convolutional neural network based features on multi-class x-ray chest images

Covid-19 is a very serious deadly disease that has been announced as a pandemic by the world health organization (WHO). The whole world is working with all its might to end Covid-19 pandemic, which puts countries in serious health and economic problems, as soon as possible. The most important of these is to correctly identify those who get the Covid-19. Methods and approaches to support the reverse transcription polymerase chain reaction (RT-PCR) test have begun to take place in the literature. In this study, chest X-ray images, which can be accessed easily and quickly, were used because the covid-19 attacked the respiratory systems. Classification performances with support vector machines have been obtained by using the features extracted with residual networks (ResNet-50), one of the convolutional neural network models, from these images. While Covid-19 detection is obtained with support vector machines (SVM)-quadratic with the highest sensitivity value of 96.35% with the 5-fold cross-validation method, the highest overall performance value has been detected with both SVM-quadratic and SVM-cubic above 99%. According to these high results, it is thought that this method, which has been studied, will help radiology specialists and reduce the rate of false detection.

COVID-19;COVID-19 Pandemic

World Health Organization;Polymerase Chain Reaction;Neural Networks;Support Vector Machine;Reverse Transcription

External

2. Detection/Diagnosis

X-Ray

2011.14871

Yes

2020-11-30

Preprint

arXiv

vidi: descriptive visual data clustering as radiologist assistant in covid-19 streamline diagnostic

In the light of the COVID-19 pandemic, deep learning methods have been widely investigated in detecting COVID-19 from chest X-rays. However, a more pragmatic approach to applying AI methods to a medical diagnosis is designing a framework that facilitates human-machine interaction and expert decision making. Studies have shown that categorization can play an essential rule in accelerating real-world decision making. Inspired by descriptive document clustering, we propose a domain-independent explanatory clustering framework to group contextually related instances and support radiologists' decision making. While most descriptive clustering approaches employ domain-specific characteristics to form meaningful clusters, we focus on model-level explanation as a more general-purpose element of every learning process to achieve cluster homogeneity. We employ DeepSHAP to generate homogeneous clusters in terms of disease severity and describe the clusters using favorable and unfavorable saliency maps, which visualize the class discriminating regions of an image. These human-interpretable maps complement radiologist knowledge to investigate the whole cluster at once. Besides, as part of this study, we evaluate a model based on VGG-19, which can identify COVID and pneumonia cases with a positive predictive value of 95% and 97%, respectively, comparable to the recent explainable approaches for COVID diagnosis.

COVID-19;COVID-19 Pandemic;Pneumonia

Proteins;Other Topics;Map;Cluster Analysis

External

2. Detection/Diagnosis

X-Ray

10.1101/2020.05.12.20099937

10.1101/2020.05.12.20099937

Yes

2020-05-14

Preprint

medRxiv

deep transfer learning-based covid-19 prediction using chest x-rays

The novel coronavirus disease (COVID-19) is spreading very rapidly across the globe because of its highly contagious nature, and is declared as a pandemic by world health organization (WHO). Scientists are endeavoring to ascertain the drugs for its efficacious treatment. Because, till now, no full-proof drug is available to cure this deadly disease. Therefore, identifying COVID-19 positive people and to quarantine them, can be an effective solution to control its spread. Many machine learning and deep learning techniques are being used quite effectively to classify positive and negative cases. In this work, a deep transfer learning-based model is proposed to classify the COVID-19 cases using chest X-rays or CT scan images of infected persons. The proposed model is based on the ensembling of DenseNet121 and SqueezeNet1.0, which is named as DeQueezeNet. The model can extract the importance of various influential features from the X-ray images, which are effectively used to classify the COVID-19 cases. The performance study of the proposed model depicts its effectiveness in terms of accuracy and precision. A comparative study has also been done with the recently published works and it is observed the performance of the proposed model is significantly better.

COVID-19

World Health Organization;Transfer Learning;Other Topics;Pharmaceutical Preparations

External

2. Detection/Diagnosis

X-Ray

2006.11988

Yes

2020-12-14

Preprint

arXiv

covid-19 image data collection: prospective predictions are the future

Across the world's coronavirus disease 2019 (COVID-19) hot spots, the need to streamline patient diagnosis and management has become more pressing than ever. As one of the main imaging tools, chest X-rays (CXRs) are common, fast, non-invasive, relatively cheap, and potentially bedside to monitor the progression of the disease. This paper describes the first public COVID-19 image data collection as well as a preliminary exploration of possible use cases for the data. This dataset currently contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of COVID-19. It was manually aggregated from publication figures as well as various web based repositories into a machine learning (ML) friendly format with accompanying dataloader code. We collected frontal and lateral view imagery and metadata such as the time since first symptoms, intensive care unit (ICU) status, survival status, intubation status, or hospital location. We present multiple possible use cases for the data such as predicting the need for the ICU, predicting patient survival, and understanding a patient's trajectory during treatment. Data can be accessed here: GitHub

COVID-19

Other Topics

Self-recorded/clinical

2. Detection/Diagnosis

Multimodal

33275588

10.1109/JBHI.2020.3042523

Yes

PMC8545178

2020-12-05

Journal Article;Research Support, Non-U.S. Gov't

Peer reviewed (PubMed)

covid-19 ct image synthesis with a conditional generative adversarial network

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has spread rapidly since December 2019. Real-time reverse transcription polymerase chain reaction (rRT-PCR) and chest computed tomography (CT) imaging both play an important role in COVID-19 diagnosis. Chest CT imaging offers the benefits of quick reporting, a low cost, and high sensitivity for the detection of pulmonary infection. Recently, deep-learning-based computer vision methods have demonstrated great promise for use in medical imaging applications, including X-rays, magnetic resonance imaging, and CT imaging. However, training a deep-learning model requires large volumes of data, and medical staff faces a high risk when collecting COVID-19 CT data due to the high infectivity of the disease. Another issue is the lack of experts available for data labeling. In order to meet the data requirements for COVID-19 CT imaging, we propose a CT image synthesis approach based on a conditional generative adversarial network that can effectively generate high-quality and realistic COVID-19 CT images for use in deep-learning-based medical imaging tasks. Experimental results show that the proposed method outperforms other state-of-the-art image synthesis methods with the generated COVID-19 CT images and indicates promising for various machine learning applications including semantic segmentation and classification.

COVID-19;Infections

IEEE J Biomed Health Inform

Radiography;Magnetic Resonance Imaging;Art;Pandemics;Semantics;Lung;Polymerase Chain Reaction;Classification;Tomography;Lung Diseases;Reverse Transcription

External

5. Post-hoc

CT

2007.08028

Yes

PMC7373136

2021-07-01

Preprint

arXiv

predicting clinical outcomes in covid-19 using radiomics and deep learning on chest radiographs: a multi-institutional study

We predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients. This two-center, retrospective study analyzed 530 deidentified CXRs from 515 COVID-19 patients treated at Stony Brook University Hospital and Newark Beth Israel Medical Center between March and August 2020. DL and machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated using patient CXRs. A novel radiomic embedding framework was also explored for outcome prediction. All results are compared against radiologist grading of CXRs (zone-wise expert severity scores). Radiomic and DL classification models had mAUCs of 0.78+/-0.02 and 0.81+/-0.04, compared with expert scores mAUCs of 0.75+/-0.02 and 0.79+/-0.05 for mechanical ventilation requirement and mortality prediction, respectively. Combined classifiers using both radiomics and expert severity scores resulted in mAUCs of 0.79+/-0.04 and 0.83+/-0.04 for each prediction task, demonstrating improvement over either artificial intelligence or radiologist interpretation alone. Our results also suggest instances where inclusion of radiomic features in DL improves model predictions, something that might be explored in other pathologies. The models proposed in this study and the prognostic information they provide might aid physician decision making and resource allocation during the COVID-19 pandemic.

COVID-19;COVID-19 Pandemic

Classification;Other Topics;Retrospective Studies;Area under Curve

Self-recorded/clinical

4. Prognosis/Treatment

X-Ray

10.1101/2020.08.24.20181339

10.1101/2020.08.24.20181339

Yes

2021-08-09

Preprint

medRxiv

diagnosis of covid-19 from x-rays using combined cnn-rnn architecture with transfer learning

The confrontation of COVID-19 pandemic has become one of the promising challenges of the world healthcare. Accurate and fast diagnosis of COVID-19 cases is essential for correct medical treatment to control this pandemic. Compared with the reverse-transcription polymerase chain reaction (RT-PCR) method, chest radiography imaging techniques are shown to be more effective to detect coronavirus. For the limitation of available medical images, transfer learning is better suited to classify patterns in medical images. This paper presents a combined architecture of convolutional neural network (CNN) and recurrent neural network (RNN) to diagnose COVID-19 from chest X-rays. The deep transfer techniques used in this experiment are VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2. CNN is used to extract complex features from samples and classified them using RNN. The VGG19-RNN architecture achieved the best performance among all the networks in terms of accuracy in our experiments. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) was used to visualize class-specific regions of images that are responsible to make decision. The system achieved promising results compared to other existing systems and might be validated in the future when more samples would be available. The experiment demonstrated a good alternative method to diagnose COVID-19 for medical staff.

COVID-19;COVID-19 Pandemic

Health Care;Transfer Learning;Architecture;Polymerase Chain Reaction;Reverse Transcription

External

2. Detection/Diagnosis

X-Ray

10.1101/2020.07.16.20155093

10.1101/2020.07.16.20155093

Yes

2020-11-06

Preprint

medRxiv

automated covid-19 detection from frontal chest x-ray images using deep learning: an online feasibility study

to evaluate the performance of Deep Learning methods to detect covid-19 from X-Ray chest images Chest X-Ray (CXR) images collected from confirmed covid-19 cases in several different centers and institutions and available online were downloaded and combined together with images of healthy patients and patients suffering from bacterial pneumonia found in other online sources. An AI image-based covid-19 classifier was developed and evaluated on the CXR images downloaded. Seven different online data sources were combined for a total of N=16,665 patients (3,156 with covid-19, 2,311 with bacterial pneumonia and 11,198 healthy patients). When half of the patients (N=8,331) where used to train the classifier leaving the other half (N=8,334) for validation, the classifier reached an Area Under the Curve (AUC) for covid-19 detection of 98.6% (detection rate of 91.8% at 1.1% false positive rate). Results were similar for other training/validation splits. AUC was close to 90% even when tested on patients from a source not used to train the classifier. Computer aided automatic covid-19 detection from CXR images showed promising results on a large cohort of patients. The classifier will be made available online for its evaluation. These results merit further evaluation through a prospective clinical study.

COVID-19;Pneumonia, Bacterial

Other Topics

External

2. Detection/Diagnosis

X-Ray

33360271

10.1016/j.compbiomed.2020.104181

Yes

PMC7831681

2020-12-29

Journal Article

Peer reviewed (PubMed)

lightweight deep learning models for detecting covid-19 from chest x-ray images

Deep learning methods have already enjoyed an unprecedented success in medical imaging problems. Similar success has been evidenced when it comes to the detection of COVID-19 from medical images, therefore deep learning approaches are considered good candidates for detecting this disease, in collaboration with radiologists and/or physicians. In this paper, we propose a new approach to detect COVID-19 via exploiting a conditional generative adversarial network to generate synthetic images for augmenting the limited amount of data available. Additionally, we propose two deep learning models following a lightweight architecture, commensurating with the overall amount of data available. Our experiments focused on both binary classification for COVID-19 vs Normal cases and multi-classification that includes a third class for bacterial pneumonia. Our models achieved a competitive performance compared to other studies in literature and also a ResNet8 model. Our best performing binary model achieved 98.7% accuracy, 100% sensitivity and 98.3% specificity, while our three-class model achieved 98.3% accuracy, 99.3% sensitivity and 98.1% specificity. Moreover, via adopting a testing protocol proposed in literature, our models proved to be more robust and reliable in COVID-19 detection than a baseline ResNet8, making them good candidates for detecting COVID-19 from posteroanterior chest X-ray images.

COVID-19;Pneumonia, Bacterial

Comput Biol Med

Other Topics

External

2. Detection/Diagnosis

X-Ray

32305937

10.1109/RBME.2020.2987975

Yes

2020-04-20

Journal Article;Review

Peer reviewed (PubMed)

review of artificial intelligence techniques in imaging data acquisition segmentation and diagnosis for covid-19

The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI can improve work efficiency by accurate delineation of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19.

COVID-19;Infections

IEEE Rev Biomed Eng

Other Topics

External

Review

Multimodal

2006.13817

Yes

2020-06-22

Preprint

arXiv

stacked convolutional neural network for diagnosis of covid-19 disease from x-ray images

Automatic and rapid screening of COVID-19 from the chest X-ray images has become an urgent need in this pandemic situation of SARS-CoV-2 worldwide in 2020. However, accurate and reliable screening of patients is a massive challenge due to the discrepancy between COVID-19 and other viral pneumonia in X-ray images. In this paper, we design a new stacked convolutional neural network model for the automatic diagnosis of COVID-19 disease from the chest X-ray images. We obtain different sub-models from the VGG19 and developed a 30-layered CNN model (named as CovNet30) during the training, and obtained sub-models are stacked together using logistic regression. The proposed CNN model combines the discriminating power of the different CNN`s sub-models and classifies chest X-ray images into COVID-19, Normal, and Pneumonia classes. In addition, we generate X-ray images dataset referred to as COVID19CXr, which includes 2764 chest x-ray images of 1768 patients from the three publicly available data repositories. The proposed stacked CNN achieves an accuracy of 92.74%, the sensitivity of 93.33%, PPV of 92.13%, and F1-score of 0.93 for the classification of X-ray images. Our proposed approach shows its superiority over the existing methods for the diagnosis of the COVID-19 from the X-ray images.

COVID-19;Pneumonia;Pneumonia, Viral

Neural Networks;Other Topics

External

2. Detection/Diagnosis

X-Ray

34175533

10.1016/j.compbiomed.2021.104605

Yes

PMC8219713

2021-06-28

Journal Article;Review

Peer reviewed (PubMed)

medical imaging and computational image analysis in covid-19 diagnosis: a review

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. This study attempts to review papers on the role of imaging and medical image computing in COVID-19 diagnosis. For this purpose, PubMed, Scopus and Google Scholar were searched to find related studies until the middle of 2021. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis, 4) to express the research limitations in this field and the methods used to overcome them. Using machine learning-based methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.

COVID-19;Communicable Diseases;Cough;Dyspnea;Fatigue;Fever

Comput Biol Med

COVID-19 Testing;Image Processing;Paper

N.A.

Review

Multimodal

2005.03059

Yes

2020-05-15

Preprint

arXiv

covidctnet: an open-source deep learning approach to identify covid-19 using ct image

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method, however, its accuracy in detection is only approx. 70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of approx. 80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source set of algorithms called CovidCTNet that successfully differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 90% compared to radiologists . The model is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. In order to facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and parametric details in an open-source format. Open-source sharing of our CovidCTNet enables developers to rapidly improve and optimize services, while preserving user privacy and data ownership.

COVID-19;Lung Diseases;Pneumonia

Polymerase Chain Reaction;Other Topics

Self-recorded/clinical

2. Detection/Diagnosis

CT

33649695

10.1007/s00521-020-05641-9

Yes

PMC7905772

2021-03-03

Journal Article

Peer reviewed (PubMed)

triage of potential covid-19 patients from chest x-ray images using hierarchical convolutional networks

The current COVID-19 pandemic has motivated the researchers to use artificial intelligence techniques for a potential alternative to reverse transcription-polymerase chain reaction due to the limited scale of testing. The chest X-ray (CXR) is one of the alternatives to achieve fast diagnosis, but the unavailability of large-scale annotated data makes the clinical implementation of machine learning-based COVID detection difficult. Another issue is the usage of ImageNet pre-trained networks which does not extract reliable feature representations from medical images. In this paper, we propose the use of hierarchical convolutional network (HCN) architecture to naturally augment the data along with diversified features. The HCN uses the first convolution layer from COVIDNet followed by the convolutional layers from well-known pre-trained networks to extract the features. The use of the convolution layer from COVIDNet ensures the extraction of representations relevant to the CXR modality. We also propose the use of ECOC for encoding multiclass problems to binary classification for improving the recognition performance. Experimental results show that HCN architecture is capable of achieving better results in comparison with the existing studies. The proposed method can accurately triage potential COVID-19 patients through CXR images for sharing the testing load and increasing the testing capacity.

COVID-19;COVID-19 Pandemic

Neural Comput Appl

Research Personnel;Polymerase Chain Reaction;Reverse Transcription

External

2. Detection/Diagnosis

X-Ray

33967366

10.1016/j.bbe.2021.04.006

Yes

PMC8084624

2021-05-11

Journal Article

Peer reviewed (PubMed)

automated detection of covid-19 from ct scans using convolutional neural networks

Under the prevailing circumstances of the global pandemic of COVID-19, early diagnosis and accurate detection of COVID-19 through tests/screening and, subsequently, isolation of the infected people would be a proactive measure. Artificial intelligence (AI) based solutions, using Convolutional Neural Network (CNN) and exploiting the Deep Learning model's diagnostic capabilities, have been studied in this paper. Transfer Learning approach, based on VGG16 and ResNet50 architectures, has been used to develop an algorithm to detect COVID-19 from CT scan images consisting of Healthy (Normal), COVID-19, and Pneumonia categories. This paper adopts data augmentation and fine-tuning techniques to improve and optimize the VGG16 and ResNet50 model. Further, stratified 5-fold cross-validation has been conducted to test the robustness and effectiveness of the model. The proposed model performs exceptionally well in case of binary classification (COVID-19 vs. Normal) with an average classification accuracy of more than 99% in both VGG16 and ResNet50 based models. In multiclass classification (COVID-19 vs. Normal vs. Pneumonia), the proposed model achieves an average classification accuracy of 86.74% and 88.52% using VGG16 and ResNet50 architectures as baseline, respectively. Experimental results show that the proposed model achieves superior performance and can be used for automated detection of COVID-19 from CT scans.

COVID-19;Pneumonia

Biocybern Biomed Eng

Algorithms;Transfer Learning;Architecture

External

2. Detection/Diagnosis

CT

2006.05274

Yes

2020-06-06

Preprint

arXiv

umls-chestnet: a deep convolutional neural network for radiological findings differential diagnoses and localizations of covid-19 in chest x-rays

In this work we present a method for the detection of radiological findings, their location and differential diagnoses from chest x-rays. Unlike prior works that focus on the detection of few pathologies, we use a hierarchical taxonomy mapped to the Unified Medical Language System (UMLS) terminology to identify 189 radiological findings, 22 differential diagnosis and 122 anatomic locations, including ground glass opacities, infiltrates, consolidations and other radiological findings compatible with COVID-19. We train the system on one large database of 92,594 frontal chest x-rays (AP or PA, standing, supine or decubitus) and a second database of 2,065 frontal images of COVID-19 patients identified by at least one positive Polymerase Chain Reaction (PCR) test. The reference labels are obtained through natural language processing of the radiological reports. On 23,159 test images, the proposed neural network obtains an AUC of 0.94 for the diagnosis of COVID-19. To our knowledge, this work uses the largest chest x-ray dataset of COVID-19 positive cases to date and is the first one to use a hierarchical labeling schema and to provide interpretability of the results, not only by using network attention methods, but also by indicating the radiological findings that have led to the diagnosis.

COVID-19

Polymerase Chain Reaction;Area under Curve

Self-recorded/clinical

2. Detection/Diagnosis

X-Ray

2004.00038

Yes

2020-03-31

Preprint

arXiv

diagnosing covid-19 pneumonia from x-ray and ct images using deep learning and transfer learning algorithms

COVID-19 (also known as 2019 Novel Coronavirus) first emerged in Wuhan, China and spread across the globe with unprecedented effect and has now become the greatest crisis of the modern era. The COVID-19 has proved much more pervasive demands for diagnosis that has driven researchers to develop more intelligent, highly responsive and efficient detection methods. In this work, we focus on proposing AI tools that can be used by radiologists or healthcare professionals to diagnose COVID-19 cases in a quick and accurate manner. However, the lack of a publicly available dataset of X-ray and CT images makes the design of such AI tools a challenging task. To this end, this study aims to build a comprehensive dataset of X-rays and CT scan images from multiple sources as well as provides a simple but an effective COVID-19 detection technique using deep learning and transfer learning algorithms. In this vein, a simple convolution neural network (CNN) and modified pre-trained AlexNet model are applied on the prepared X-rays and CT scan images dataset. The result of the experiments shows that the utilized models can provide accuracy up to 98 % via pre-trained network and 94.1 % accuracy by using the modified CNN.

COVID-19;Pneumonia

Health Care;Algorithms;Transfer Learning;Research Personnel

External

2. Detection/Diagnosis

Multimodal

2004.02060

Yes

2020-05-20

Preprint

arXiv

finding covid-19 from chest x-rays using deep learning on a small dataset

Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We have obtained 122 chest X-rays of COVID-19 and over 4,000 chest X-rays of viral and bacterial pneumonia. A pretrained deep convolutional neural network has been tuned on 102 COVID-19 cases and 102 other pneumonia cases in a 10-fold cross validation. The results were all 102 COVID-19 cases were correctly classified and there were 8 false positives resulting in an AUC of 0.997. On a test set of 20 unseen COVID-19 cases all were correctly classified and more than 95% of 4171 other pneumonia examples were correctly classified. This study has flaws, most critically a lack of information about where in the disease process the COVID-19 cases were and the small data set size. More COVID-19 case images will enable a better answer to the question of how useful chest X-rays can be for diagnosing COVID-19 (so please send them).

COVID-19;Pneumonia;Pneumonia, Bacterial

Other Topics

External

2. Detection/Diagnosis

X-Ray

2009.05436

Yes

2021-02-28

Preprint

arXiv

semi-supervised active learning for covid-19 lung ultrasound multi-symptom classification

Ultrasound (US) is a non-invasive yet effective medical diagnostic imaging technique for the COVID-19 global pandemic. However, due to complex feature behaviors and expensive annotations of US images, it is difficult to apply Artificial Intelligence (AI) assisting approaches for lung's multi-symptom (multi-label) classification. To overcome these difficulties, we propose a novel semi-supervised Two-Stream Active Learning (TSAL) method to model complicated features and reduce labeling costs in an iterative procedure. The core component of TSAL is the multi-label learning mechanism, in which label correlations information is used to design multi-label margin (MLM) strategy and confidence validation for automatically selecting informative samples and confident labels. On this basis, a multi-symptom multi-label (MSML) classification network is proposed to learn discriminative features of lung symptoms, and a human-machine interaction is exploited to confirm the final annotations that are used to fine-tune MSML with progressively labeled data. Moreover, a novel lung US dataset named COVID19-LUSMS is built, currently containing 71 clinical patients with 6,836 images sampled from 678 videos. Experimental evaluations show that TSAL using only 20% data can achieve superior performance to the baseline and the state-of-the-art. Qualitatively, visualization of both attention map and sample distribution confirms the good consistency with the clinic knowledge.

COVID-19

Diagnostic Imaging;Art;Other Topics;Map

Self-recorded/clinical

2. Detection/Diagnosis

Ultrasound

2010.16043

Yes

2020-10-29

Preprint

arXiv

ct-caps: feature extraction-based automated framework for covid-19 disease identification from chest ct scans using capsule networks

The global outbreak of the novel corona virus (COVID-19) disease has drastically impacted the world and led to one of the most challenging crisis across the globe since World War II. The early diagnosis and isolation of COVID-19 positive cases are considered as crucial steps towards preventing the spread of the disease and flattening the epidemic curve. Chest Computed Tomography (CT) scan is a highly sensitive, rapid, and accurate diagnostic technique that can complement Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Recently, deep learning-based models, mostly based on Convolutional Neural Networks (CNN), have shown promising diagnostic results. CNNs, however, are incapable of capturing spatial relations between image instances and require large datasets. Capsule Networks, on the other hand, can capture spatial relations, require smaller datasets, and have considerably fewer parameters. In this paper, a Capsule network framework, referred to as the "CT-CAPS", is presented to automatically extract distinctive features of chest CT scans. These features, which are extracted from the layer before the final capsule layer, are then leveraged to differentiate COVID-19 from Non-COVID cases. The experiments on our in-house dataset of 307 patients show the state-of-the-art performance with the accuracy of 90.8%, sensitivity of 94.5%, and specificity of 86.0%.

COVID-19

Art;Disease Outbreaks;Polymerase Chain Reaction;Tomography;Early Diagnosis;Reverse Transcription

External

2. Detection/Diagnosis

CT

33729944

10.1109/TCBB.2021.3066331

Yes

PMC9647721

2021-03-18

Journal Article

Peer reviewed (PubMed)

soda: detecting covid-19 in chest x-rays with semi-supervised open set domain adaptation

Due to the shortage of COVID-19 viral testing kits, radiology is used to complement the screening process. Deep learning methods are promising in automatically detecting COVID-19 disease in chest x-ray images. Most of these works first train a Convolutional Neural Network (CNN) on an existing large-scale chest x-ray image dataset and then fine-tune the model on the newly collected COVID-19 chest x-ray dataset, often at a much smaller scale. However, simple fine-tuning may lead to poor performance due to two issues, firstly the large domain shift present in chest x-ray datasets and secondly the relatively small scale of the COVID-19 chest x-ray dataset. In an attempt to address these issues, we formulate the problem of COVID-19 chest x-ray image classification in a semi-supervised open set domain adaptation setting and propose a novel domain adaptation method, Semi-supervised Open set Domain Adversarial network (SODA). SODA is designed to align the data distributions across different domains in the general domain space and also in the common subspace of source and target data. In our experiments, SODA achieves a leading classification performance compared with recent state-of-the-art models in separating COVID-19 with common pneumonia. We also present results showing that SODA produces better pathology localizations.

COVID-19;Pneumonia

IEEE/ACM Trans Comput Biol Bioinform

Art;Other Topics

External

2. Detection/Diagnosis

X-Ray

33192206

10.1016/j.asoc.2020.106885

Yes

PMC7647900

2020-11-17

Journal Article

Peer reviewed (PubMed)

the ensemble deep learning model for novel covid-19 on ct images

The rapid detection of the novel coronavirus disease, COVID-19, has a positive effect on preventing propagation and enhancing therapeutic outcomes. This article focuses on the rapid detection of COVID-19. We propose an ensemble deep learning model for novel COVID-19 detection from CT images. 2933 lung CT images from COVID-19 patients were obtained from previous publications, authoritative media reports, and public databases. The images were preprocessed to obtain 2500 high-quality images. 2500 CT images of lung tumor and 2500 from normal lung were obtained from a hospital. Transfer learning was used to initialize model parameters and pretrain three deep convolutional neural network models: AlexNet, GoogleNet, and ResNet. These models were used for feature extraction on all images. Softmax was used as the classification algorithm of the fully connected layer. The ensemble classifier EDL-COVID was obtained via relative majority voting. Finally, the ensemble classifier was compared with three component classifiers to evaluate accuracy, sensitivity, specificity, F value, and Matthews correlation coefficient. The results showed that the overall classification performance of the ensemble model was better than that of the component classifier. The evaluation indexes were also higher. This algorithm can better meet the rapid detection requirements of the novel coronavirus disease COVID-19.

COVID-19;Neoplasms

Appl Soft Comput

Transfer Learning;Algorithms;Sensitivity and Specificity;Lung;Neural Networks

External

2. Detection/Diagnosis

CT

33015100

10.3389/fmed.2020.00550

Yes

PMC7461795

2020-10-06

Journal Article

Peer reviewed (PubMed)

the performance of deep neural networks in differentiating chest x-rays of covid-19 patients from other bacterial and viral pneumonias

Chest radiography is a critical tool in the early detection, management planning, and follow-up evaluation of COVID-19 pneumonia; however, in smaller clinics around the world, there is a shortage of radiologists to analyze large number of examinations especially performed during a pandemic. Limited availability of high-resolution computed tomography and real-time polymerase chain reaction in developing countries and regions of high patient turnover also emphasizes the importance of chest radiography as both a screening and diagnostic tool. In this paper, we compare the performance of 17 available deep learning algorithms to help identify imaging features of COVID19 pneumonia. We utilize an existing diagnostic technology (chest radiography) and preexisting neural networks (DarkNet-19) to detect imaging features of COVID-19 pneumonia. Our approach eliminates the extra time and resources needed to develop new technology and associated algorithms, thus aiding the front-line healthcare workers in the race against the COVID-19 pandemic. Our results show that DarkNet-19 is the optimal pre-trained neural network for the detection of radiographic features of COVID-19 pneumonia, scoring an overall accuracy of 94.28% over 5,854 X-ray images. We also present a custom visualization of the results that can be used to highlight important visual biomarkers of the disease and disease progression.

COVID-19;COVID-19 Pandemic;Disease Progression;Pneumonia;Pneumonia, Viral

Front Med (Lausanne)

Transfer Learning;Algorithms;Polymerase Chain Reaction;Tomography;Real-Time Polymerase Chain Reaction

Self-recorded/clinical

2. Detection/Diagnosis

X-Ray

10.1101/2020.04.13.20063461

10.1101/2020.04.13.20063461

Yes

2020-04-17

Preprint

medRxiv

accurate prediction of covid-19 using chest x-ray images through deep feature learning model with smote and machine learning classifiers

According to the World Health Organization (WHO), the coronavirus (COVID-19) pandemic is putting even the best healthcare systems across the world under tremendous pressure. The early detection of this type of virus will help in relieving the pressure of the healthcare systems. Chest X-rays has been playing a crucial role in the diagnosis of diseases like Pneumonia. As COVID-19 is a type of influenza, it is possible to diagnose using this imaging technique. With rapid development in the area of Machine Learning (ML) and Deep learning, there had been intelligent systems to classify between Pneumonia and Normal patients. This paper proposes the machine learning-based classification of the extracted deep feature using ResNet152 with COVID-19 and Pneumonia patients on chest X-ray images. SMOTE is used for balancing the imbalanced data points of COVID-19 and Normal patients. This non-invasive and early prediction of novel coronavirus (COVID-19) by analyzing chest X-rays can further be used to predict the spread of the virus in asymptomatic patients. The model is achieving an accuracy of 0.973 on Random Forest and 0.977 using XGBoost predictive classifiers. The establishment of such an approach will be useful to predict the outbreak early, which in turn can aid to control it effectively.

COVID-19;COVID-19 Pandemic;Influenza, Human;Pneumonia

World Health Organization;Health Care;Disease Outbreaks;Random Forest

External

2. Detection/Diagnosis

X-Ray

32719039

10.1136/annrheumdis-2020-218048

Yes

PMC7456556

2020-07-29

Comparative Study;Journal Article

Peer reviewed (PubMed)

lung involvement in macrophage activation syndrome and severe covid-19: results from a cross-sectional study to assess clinical laboratory and artificial intelligence-radiological differences

To evaluate the clinical pictures, laboratory tests and imaging of patients with lung involvement, either from severe COVID-19 or macrophage activation syndrome (MAS), in order to assess how similar these two diseases are. The present work has been designed as a cross-sectional single-centre study to compare characteristics of patients with lung involvement either from MAS or severe COVID-19. Chest CT scans were assessed by using an artificial intelligence (AI)-based software. Ten patients with MAS and 47 patients with severe COVID-19 with lung involvement were assessed. Although all patients showed fever and dyspnoea, patients with MAS were characterised by thrombocytopaenia, whereas patients with severe COVID-19 were characterised by lymphopaenia and neutrophilia. Higher values of H-score characterised patients with MAS when compared with severe COVID-19. AI-reconstructed images of chest CT scan showed that apical, basal, peripheral and bilateral distributions of ground-glass opacities (GGOs), as well as apical consolidations, were more represented in severe COVID-19 than in MAS. C reactive protein directly correlated with GGOs extension in both diseases. Furthermore, lymphopaenia inversely correlated with GGOs extension in severe COVID-19. Our data could suggest laboratory and radiological differences between MAS and severe COVID-19, paving the way for further hypotheses to be investigated in future confirmatory studies.

COVID-19;Fever;Macrophage Activation Syndrome

Ann Rheum Dis

Coronavirus Infections;C-Reactive Protein;Retrospective Studies

Self-recorded/clinical

3. Monitoring/Severity assessment

CT

10.1101/2020.05.05.20091561

10.1101/2020.05.05.20091561

Yes

2020-05-08

Preprint

medRxiv

ai based chest x-ray (cxr) scan texture analysis algorithm for digital test of covid-19 patients

Chest Imaging in COVID-19 patient management is becoming an essential tool for controlling the pandemic that is gripping the international community. It is already indicated in patients with COVID-19 and worsening respiratory status. The rapid spread of the pandemic to all continents, albeit with a nonuniform community transmission, necessitates chest imaging for medical triage of patients presenting moderate-severe clinical COVID-19 features. This paper reports the development of innovative machine learning schemes for the analysis of Chest X-Ray (CXR) scan images of COVID-19 patients in almost real-time, demonstrating significantly high accuracy in identifying COVID-19 infection. The performance testing was conducted on a combined dataset comprising CXRs of positive COVID-19 patients, patients with various viral and bacterial infections, as well as persons with a clear chest. The test resulted in successfully distinguishing CXR COVID-19 infection from the other cases with an average accuracy of 94.43%, sensitivity 95% and specificity 93.86%.The development of efficient automatic AI texture analysis schemes for classification of chest X-Ray of COVID-19 patients with highest accuracy with equally low false negative and positive rates. Decisions would be supported by visual evidence viewable by clinician and help speed up the initial assessment process of new suspected cases, especially in a resource-constrained environment.

Bacterial Infections;COVID-19;Infections

Health Care;Sensitivity and Specificity

External

2. Detection/Diagnosis

X-Ray

33362346

10.1002/ima.22525

Yes

PMC7753711

2020-12-29

Journal Article

Peer reviewed (PubMed)

an integrated feature frame work for automated segmentation of covid-19 infection from lung ct images

The novel coronavirus disease (SARS-CoV-2 or COVID-19) is spreading across the world and is affecting public health and the world economy. Artificial Intelligence (AI) can play a key role in enhancing COVID-19 detection. However, lung infection by COVID-19 is not quantifiable due to a lack of studies and the difficulty involved in the collection of large datasets. Segmentation is a preferred technique to quantify and contour the COVID-19 region on the lungs using computed tomography (CT) scan images. To address the dataset problem, we propose a deep neural network (DNN) model trained on a limited dataset where features are selected using a region-specific approach. Specifically, we apply the Zernike moment (ZM) and gray level co-occurrence matrix (GLCM) to extract the unique shape and texture features. The feature vectors computed from these techniques enable segmentation that illustrates the severity of the COVID-19 infection. The proposed algorithm was compared with other existing state-of-the-art deep neural networks using the Radiopedia and COVID-19 CT Segmentation datasets presented specificity, sensitivity, sensitivity, mean absolute error (MAE), enhance-alignment measure (EMφ), and structure measure (S m) of 0.942, 0.701, 0.082, 0.867, and 0.783, respectively. The metrics demonstrate the performance of the model in quantifying the COVID-19 infection with limited datasets.

COVID-19;Infections

Int J Imaging Syst Technol

Coronavirus Infections;Art;Public Health;Algorithms;Tomography;Lung Diseases

External

Segmentation-only

CT

32344309

10.1016/j.mehy.2020.109761

Yes

PMC7179515

2020-04-29

Journal Article

Peer reviewed (PubMed)

covidiagnosis-net: deep bayes-squeezenet based diagnosis of the coronavirus disease 2019 (covid-19) from x-ray images

The Coronavirus Disease 2019 (COVID-19) outbreak has a tremendous impact on global health and the daily life of people still living in more than two hundred countries. The crucial action to gain the force in the fight of COVID-19 is to have powerful monitoring of the site forming infected patients. Most of the initial tests rely on detecting the genetic material of the coronavirus, and they have a poor detection rate with the time-consuming operation. In the ongoing process, radiological imaging is also preferred where chest X-rays are highlighted in the diagnosis. Early studies express the patients with an abnormality in chest X-rays pointing to the presence of the COVID-19. On this motivation, there are several studies cover the deep learning-based solutions to detect the COVID-19 using chest X-rays. A part of the existing studies use non-public datasets, others perform on complicated Artificial Intelligent (AI) structures. In our study, we demonstrate an AI-based structure to outperform the existing studies. The SqueezeNet that comes forward with its light network design is tuned for the COVID-19 diagnosis with Bayesian optimization additive. Fine-tuned hyperparameters and augmented dataset make the proposed network perform much better than existing network designs and to obtain a higher COVID-19 diagnosis accuracy.

COVID-19

Med Hypotheses

Coronavirus Infections;Disease Outbreaks;Image Processing;Neural Networks;Early Diagnosis

External

2. Detection/Diagnosis

X-Ray

10.1101/2020.10.13.20212258

10.1101/2020.10.13.20212258

Yes

2020-10-22

Preprint

medRxiv

development of a deep learning classifier to accurately distinguish covid-19 from look-a-like pathology on lung ultrasound

Lung ultrasound (LUS) is a portable, low cost respiratory imaging tool but is challenged by user dependence and lack of diagnostic specificity. It is unknown whether the advantages of LUS implementation could be paired with deep learning techniques to match or exceed human-level, diagnostic specificity among similar appearing, pathological LUS images. A convolutional neural network was trained on LUS images with B lines of different etiologies. CNN diagnostic performance, as validated using a 10% data holdback set was compared to surveyed LUS-competent physicians. Two tertiary Canadian hospitals. 600 LUS videos (121,381 frames) of B lines from 243 distinct patients with either 1) COVID-19, Non-COVID acute respiratory distress syndrome (NCOVID) and 3) Hydrostatic pulmonary edema (HPE). The trained CNN performance on the independent dataset showed an ability to discriminate between COVID (AUC 1.0), NCOVID (AUC 0.934) and HPE (AUC 1.0) pathologies. This was significantly better than physician ability (AUCs of 0.697, 0.704, 0.967 for the COVID, NCOVID and HPE classes, respectively), p < 0.01. A deep learning model can distinguish similar appearing LUS pathology, including COVID-19, that cannot be distinguished by humans. The performance gap between humans and the model suggests that subvisible biomarkers within ultrasound images could exist and multi-center research is merited.

COVID-19;Pulmonary Edema;Respiratory Distress Syndrome, Acute

Other Topics

Self-recorded/clinical

2. Detection/Diagnosis

Ultrasound

2004.03042

Yes

2020-09-07

Preprint

arXiv

covid-mobilexpert: on-device covid-19 patient triage and follow-up using chest x-rays

During the COVID-19 pandemic, there has been an emerging need for rapid, dedicated, and point-of-care COVID-19 patient disposition techniques to optimize resource utilization and clinical workflow. In view of this need, we present COVID-MobileXpert: a lightweight deep neural network (DNN) based mobile app that can use chest X-ray (CXR) for COVID-19 case screening and radiological trajectory prediction. We design and implement a novel three-player knowledge transfer and distillation (KTD) framework including a pre-trained attending physician (AP) network that extracts CXR imaging features from a large scale of lung disease CXR images, a fine-tuned resident fellow (RF) network that learns the essential CXR imaging features to discriminate COVID-19 from pneumonia and/or normal cases with a small amount of COVID-19 cases, and a trained lightweight medical student (MS) network to perform on-device COVID-19 patient triage and follow-up. To tackle the challenge of vastly similar and dominant fore- and background in medical images, we employ novel loss functions and training schemes for the MS network to learn the robust features. We demonstrate the significant potential of COVID-MobileXpert for rapid deployment via extensive experiments with diverse MS architecture and tuning parameter settings. The source codes for cloud and mobile based models are available from the following url: GitHub

COVID-19;COVID-19 Pandemic;Lung Diseases;Pneumonia

Other Topics

External

2. Detection/Diagnosis

X-Ray

10.1101/2020.07.11.20149112

10.1101/2020.07.11.20149112

Yes

2020-07-11

Preprint

medRxiv

reconet: multi-level preprocessing of chest x-rays for covid-19 detection using convolutional neural networks

Life-threatening COVID-19 detection from radiomic features has become a dire need of the present time for infection control and socio-economic crisis management around the world. In this paper, a novel convolutional neural network (CNN) architecture, ReCoNet (residual image-based COVID-19 detection network), is proposed for COVID-19 detection. This is achieved from chest X-ray (CXR) images shedding light on the preprocessing task considered to be very useful for enhancing the COVID-19 fingerprints. The proposed modular architecture consists of a CNN-based multi-level preprocessing filter block in cascade with a multi-layer CNN-based feature extractor and a classification block. A multi-task learning loss function is adopted for optimization of the preprocessing block trained end-to-end with the rest of the proposed network. Additionally, a data augmentation technique is applied for boosting the network performance. The whole network when pre-trained end-to-end on the CheXpert open source dataset, and trained and tested with the COVIDx dataset of 15,134 original CXR images yielded an overall benchmark accuracy, sensitivity, and specificity of 97.48%, 96.39%, and 97.53%, respectively. The immense potential of ReCoNet may be exploited in clinics for rapid and safe detection of COVID-19 globally, in particular in the low and middle income countries where RT-PCR labs and/or kits are in a serious crisis.

COVID-19;Infections

Polymerase Chain Reaction;Other Topics

External

2. Detection/Diagnosis

X-Ray

2003.09439

Yes

2020-09-22

Preprint

arXiv

roam: random layer mixup for semi-supervised learning in medical imaging

Medical image segmentation is one of the major challenges addressed by machine learning methods. Yet, deep learning methods profoundly depend on a large amount of annotated data, which is time-consuming and costly. Though, semi-supervised learning methods approach this problem by leveraging an abundant amount of unlabeled data along with a small amount of labeled data in the training process. Recently, MixUp regularizer has been successfully introduced to semi-supervised learning methods showing superior performance. MixUp augments the model with new data points through linear interpolation of the data at the input space. We argue that this option is limited. Instead, we propose ROAM, a \textitrandom layer mixup, which encourages the network to be less confident for interpolated data points at randomly selected space. ROAM generates more data points that have never seen before, and hence it avoids over-fitting and enhances the generalization ability. We conduct extensive experiments to validate our method on publicly available datasets on whole-brain image segmentation (MALC) and Lung and Infection segmentation in COVID-19. ROAM achieves state-of-the-art (SOTA) results in fully supervised and semi-supervised settings with a relative improvement of up to 2.40\% and 16.50\%, respectively for the whole-brain segmentation. Similarly, ROAM achieves superior performance for COVID-19 segmentation beating SOTA SSL settings.

COVID-19;Infections

Art;Other Topics

External

Segmentation-only

Multimodal

2011.05746

Yes

2020-11-11

Preprint

arXiv

classification of covid-19 in chest ct images using convolutional support vector machines

Coronavirus 2019 (COVID-19), which emerged in Wuhan, China and affected the whole world, has cost the lives of thousands of people. Manual diagnosis is inefficient due to the rapid spread of this virus. For this reason, automatic COVID-19 detection studies are carried out with the support of artificial intelligence algorithms. In this study, a deep learning model that detects COVID-19 cases with high performance is presented. The proposed method is defined as Convolutional Support Vector Machine (CSVM) and can automatically classify Computed Tomography (CT) images. Unlike the pre-trained Convolutional Neural Networks (CNN) trained with the transfer learning method, the CSVM model is trained as a scratch. To evaluate the performance of the CSVM method, the dataset is divided into two parts as training and testing . The CSVM model consists of blocks containing three different numbers of SVM kernels. When the performance of pre-trained CNN networks and CSVM models is assessed, CSVM (7x7, 3x3, 1x1) model shows the highest performance with 94.03% ACC, 96.09% SEN, 92.01% SPE, 92.19% PRE, 94.10% F1-Score, 88.15% MCC and 88.07% Kappa metric values. The proposed method is more effective than other methods. It has proven in experiments performed to be an inspiration for combating COVID and for future studies.

COVID-19

Transfer Learning;Algorithms;Tomography

External

2. Detection/Diagnosis

CT

32406829

10.1109/TMI.2020.2994459

Yes

2020-05-15

Journal Article

Peer reviewed (PubMed)

deep learning for classification and localization of covid-19 markers in point-of-care lung ultrasound

Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, video-level, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data.

COVID-19;COVID-19 Pandemic;Lung Diseases

IEEE Trans Med Imaging

Coronavirus Infections;Art;Point-of-Care Systems;Ultrasonography;Lung Diseases;Masks

Self-recorded/clinical

3. Monitoring/Severity assessment

Ultrasound

33518813

10.1016/j.patcog.2021.107826

Yes

PMC7833525

2021-02-02

Journal Article

Peer reviewed (PubMed)

momentum contrastive learning for few-shot covid-19 diagnosis from chest ct images

The current pandemic, caused by the outbreak of a novel coronavirus (COVID-19) in December 2019, has led to a global emergency that has significantly impacted economies, healthcare systems and personal wellbeing all around the world. Controlling the rapidly evolving disease requires highly sensitive and specific diagnostics. While RT-PCR is the most commonly used, it can take up to eight hours, and requires significant effort from healthcare professionals. As such, there is a critical need for a quick and automatic diagnostic system. Diagnosis from chest CT images is a promising direction. However, current studies are limited by the lack of sufficient training samples, as acquiring annotated CT images is time-consuming. To this end, we propose a new deep learning algorithm for the automated diagnosis of COVID-19, which only requires a few samples for training. Specifically, we use contrastive learning to train an encoder which can capture expressive feature representations on large and publicly available lung datasets and adopt the prototypical network for classification. We validate the efficacy of the proposed model in comparison with other competing methods on two publicly available and annotated COVID-19 CT datasets. Our results demonstrate the superior performance of our model for the accurate diagnosis of COVID-19 based on chest CT images.

COVID-19

Pattern Recognit

Health Care;Disease Outbreaks;Polymerase Chain Reaction;Other Topics

External

2. Detection/Diagnosis

CT

2005.04562

Yes

2020-05-25

Preprint

arXiv

fast and accurate detection of covid-19-related pneumonia from chest x-ray images with novel deep learning model

Novel coronavirus disease has spread rapidly worldwide. As recent radiological literatures on Covid-19 related pneumonia is primarily focused on CT findings, the American College of Radiology (ACR) recommends using portable chest X-radiograph (CXR). A tool to assist for detection and monitoring of Covid-19 cases from CXR is highly required. To develop a fully automatic framework to detect Covid-19 related pneumonia using CXR images and evaluate its performance. : In this study, a novel deep learning model, named CovIDNet (Covid-19 Indonesia Neural-Network), was developed to extract visual features from chest x-ray images for the detection of Covid-19 related pneumonia. The model was trained and validated by chest x-rays datasets collected from several open source provided by GitHub and Kaggle. In the validation stage using open-source data, the accuracy to recognize Covid-19 and others classes reaches 98.44%, that is, 100% Covid-19 precision and 97% others precision. The use of the model to classify Covid-19 and other pathologies might slightly decrease the accuracy. Although SoftMax was used to handle classification bias, this indicates the benefit of additional training upon the introduction of new set of data. The model has been tested and get 98.4% accuracy for open source datasets, the sensitivity and specificity are 100% and 96.97%, respectively.

COVID-19;Pneumonia

Other Topics

External

2. Detection/Diagnosis

X-Ray

32840070

10.7507/1001-5515.202005056

Yes

2020-08-26

Journal Article

Peer reviewed (PubMed)

research on covid-19 detection method based on depthwise separable densenet in chest x-ray images

Coronavirus disease 2019 (COVID-19) has spread rapidly around the world. In order to diagnose COVID-19 more quickly, in this paper, a depthwise separable DenseNet was proposed. The paper constructed a deep learning model with 2 905 chest X-ray images as experimental dataset. In order to enhance the contrast, the contrast limited adaptive histogram equalization (CLAHE) algorithm was used to preprocess the X-ray image before network training, then the images were put into the training network and the parameters of the network were adjusted to the optimal. Meanwhile, Leaky ReLU was selected as the activation function. VGG16, ResNet18, ResNet34, DenseNet121 and SDenseNet models were used to compare with the model proposed in this paper. Compared with ResNet34, the proposed classification model of pneumonia had improved 2.0%, 2.3% and 1.5% in accuracy, sensitivity and specificity respectively. Compared with the SDenseNet network without depthwise separable convolution, number of parameters of the proposed model was reduced by 43.9%, but the classification effect did not decrease. It can be found that the proposed DWSDenseNet has a good classification effect on the COVID-19 chest X-ray images dataset. Under the condition of ensuring the accuracy as much as possible, the depthwise separable convolution can effectively reduce number of parameters of the model.

COVID-19;Pneumonia

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi

Other Topics

External

2. Detection/Diagnosis

X-Ray

2012.14204

Yes

2020-12-29

Preprint

arXiv

screening covid-19 based on ct/cxr images and building a publicly available ct-scan dataset of covid-19

The rapid outbreak of COVID-19 threatens humans life all around the world. Due to insufficient diagnostic infrastructures, developing an accurate, efficient, inexpensive, and quick diagnostic tool is of great importance. As chest radiography, such as chest X-ray (CXR) and CT computed tomography (CT), is a possible way for screening COVID-19, developing an automatic image classification tool is immensely helpful for detecting the patients with COVID-19. To date, researchers have proposed several different screening methods; however, none of them could achieve a reliable and highly sensitive performance yet. The main drawbacks of current methods are the lack of having enough training data, low generalization performance, and a high rate of false-positive detection. To tackle such limitations, this study firstly builds a large-size publicly available CT-scan dataset, consisting of more than 13k CT-images of more than 1000 individuals, in which 8k images are taken from 500 patients infected with COVID-19. Secondly, we propose a deep learning model for screening COVID-19 using our proposed CT dataset and report the baseline results. Finally, we extend the proposed CT model for screening COVID-19 from CXR images using a transfer learning approach. The experimental results show that the proposed CT and CXR methods achieve the AUC scores of 0.886 and 0.984 respectively.

COVID-19

Transfer Learning;Research Personnel;Disease Outbreaks;Tomography;Area under Curve

Self-recorded/clinical

2. Detection/Diagnosis

CT

32845849

10.1109/JBHI.2020.3019505

Yes

2020-08-28

Journal Article;Research Support, Non-U.S. Gov't

Peer reviewed (PubMed)

adaptive feature selection guided deep forest for covid-19 classification with chest ct

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE), AUC, precision and F1-score achieved by our method are 91.79%, 93.05%, 89.95%, 96.35%, 93.10% and 93.07%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.

COVID-19;Pneumonia

IEEE J Biomed Health Inform

Radiography;Coronavirus Infections;Disease Outbreaks;COVID-19 Testing;Sensitivity and Specificity;Neural Networks;Paper;Area under Curve

Self-recorded/clinical

2. Detection/Diagnosis

CT

34075355

10.1007/s42979-021-00695-5

Yes

PMC8152712

2021-06-03

Journal Article

Peer reviewed (PubMed)

chest x-ray classification using deep learning for automated covid-19 screening

In today's world, we find ourselves struggling to fight one of the worst pandemics in the history of humanity known as COVID-2019 caused by a coronavirus. When the virus reaches the lungs, we observe ground-glass opacity in the chest X-ray due to fibrosis in the lungs. Due to the significant differences between X-ray images of an infected and non-infected person, artificial intelligence techniques can be used to identify the presence and severity of the infection. We propose a classification model that can analyze the chest X-rays and help in the accurate diagnosis of COVID-19. Our methodology classifies the chest X-rays into four classes viz. normal, pneumonia, tuberculosis (TB), and COVID-19. Further, the X-rays indicating COVID-19 are classified on a severity-basis into mild, medium, and severe. The deep learning model used for the classification of pneumonia, TB, and normal is VGG-16 with a test accuracy of 95.9 %. For the segregation of normal pneumonia and COVID-19, the DenseNet-161 was used with a test accuracy of 98.9 %, whereas the ResNet-18 worked best for severity classification achieving a test accuracy up to 76 %. Our approach allows mass screening of the people using X-rays as a primary validation for COVID-19. The online version contains supplementary material available at 10.1007/s42979-021-00695-5.

COVID-19;Fibrosis;Infections;Pneumonia;Tuberculosis

SN Comput Sci

Tuberculosis;Fibrosis

Self-recorded/clinical

2. Detection/Diagnosis

X-Ray

2009.01657

Yes

2020-08-27

Preprint

arXiv

a free web service for fast covid-19 classification of chest x-ray images

The coronavirus outbreak became a major concern for society worldwide. Technological innovation and ingenuity are essential to fight COVID-19 pandemic and bring us one step closer to overcome it. Researchers over the world are working actively to find available alternatives in different fields, such as the Healthcare System, pharmaceutic, health prevention, among others. With the rise of artificial intelligence (AI) in the last 10 years, IA-based applications have become the prevalent solution in different areas because of its higher capability, being now adopted to help combat against COVID-19. This work provides a fast detection system of COVID-19 characteristics in X-Ray images based on deep learning (DL) techniques. This system is available as a free web deployed service for fast patient classification, alleviating the high demand for standards method for COVID-19 diagnosis. It is constituted of two deep learning models, one to differentiate between X-Ray and non-X-Ray images based on Mobile-Net architecture, and another one to identify chest X-Ray images with characteristics of COVID-19 based on the DenseNet architecture. For real-time inference, it is provided a pair of dedicated GPUs, which reduce the computational time. The whole system can filter out non-chest X-Ray images, and detect whether the X-Ray presents characteristics of COVID-19, highlighting the most sensitive regions.

COVID-19;COVID-19 Pandemic

Health Care;Research Personnel;Architecture;Disease Outbreaks

External

2. Detection/Diagnosis

X-Ray

2009.06116

10.3390/app11020672

Yes

2020-09-13

Preprint

arXiv

accelerating covid-19 differential diagnosis with explainable ultrasound image analysis

Controlling the COVID-19 pandemic largely hinges upon the existence of fast, safe, and highly-available diagnostic tools. Ultrasound, in contrast to CT or X-Ray, has many practical advantages and can serve as a globally-applicable first-line examination technique. We provide the largest publicly available lung ultrasound (US) dataset for COVID-19 consisting of 106 videos from three classes (COVID-19, bacterial pneumonia, and healthy controls); curated and approved by medical experts. On this dataset, we perform an in-depth study of the value of deep learning methods for differential diagnosis of COVID-19. We propose a frame-based convolutional neural network that correctly classifies COVID-19 US videos with a sensitivity of 0.98+-0.04 and a specificity of 0.91+-08 (frame-based sensitivity 0.93+-0.05, specificity 0.87+-0.07). We further employ class activation maps for the spatio-temporal localization of pulmonary biomarkers, which we subsequently validate for human-in-the-loop scenarios in a blindfolded study with medical experts. Aiming for scalability and robustness, we perform ablation studies comparing mobile-friendly, frame- and video-based architectures and show reliability of the best model by aleatoric and epistemic uncertainty estimates. We hope to pave the road for a community effort toward an accessible, efficient and interpretable screening method and we have started to work on a clinical validation of the proposed method. Data and code are publicly available.

COVID-19;COVID-19 Pandemic;Pneumonia, Bacterial

Specificity;Architecture;Map

Self-recorded/clinical

2. Detection/Diagnosis

Ultrasound

2007.10785

Yes

2020-07-27

Preprint

arXiv

automated detection and forecasting of covid-19 using deep learning techniques: a review

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA. This virus has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming, demanding, and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methodologies, deep learning (DL) networks have gained much popularity compared to traditional machine learning (ML) methods. Unlike ML techniques, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and automated segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL techniques is presented. Lastly, the challenges faced in the automated detection of COVID-19 using DL techniques and directions for future research are discussed.

COVID-19

Other Topics

External

Review

Multimodal

2005.01578

10.1007/s42600-021-00132-9

Yes

2021-01-12

Preprint

arXiv

a deep convolutional neural network for covid-19 detection using chest x-rays

We present image classifiers based on Dense Convolutional Networks and transfer learning to classify chest X-ray images according to three labels: COVID-19, pneumonia and normal. We fine-tuned neural networks pretrained on ImageNet and applied a twice transfer learning approach, using NIH ChestX-ray14 dataset as an intermediate step. We also suggested a novelty called output neuron keeping, which changes the twice transfer learning technique. In order to clarify the modus operandi of the models, we used Layer-wise Relevance Propagation (LRP) to generate heatmaps. We were able to reach test accuracy of 100% on our test dataset. Twice transfer learning and output neuron keeping showed promising results improving performances, mainly in the beginning of the training process. Although LRP revealed that words on the X-rays can influence the networks' predictions, we discovered this had only a very small effect on accuracy. Although clinical studies and larger datasets are still needed to further ensure good generalization, the state-of-the-art performances we achieved show that, with the help of artificial intelligence, chest X-rays can become a cheap and accurate auxiliary method for COVID-19 diagnosis. Heatmaps generated by LRP improve the interpretability of the deep neural networks and indicate an analytical path for future research on diagnosis. Twice transfer learning with output neuron keeping improved performances.

COVID-19;Pneumonia

Art;Transfer Learning;Other Topics

External

2. Detection/Diagnosis

X-Ray

33705321

10.1109/TCBB.2021.3065361

Yes

PMC8851430

2021-03-12

Journal Article;Research Support, Non-U.S. Gov't

Peer reviewed (PubMed)

deep learning enables accurate diagnosis of novel coronavirus (covid-19) with ct images

A novel coronavirus (COVID-19) recently emerged as an acute respiratory syndrome, and has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Here, we have collected chest CT scans of 88 patients diagnosed with COVID-19 from hospitals of two provinces in China, 100 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the data, a deep learning-based CT diagnosis system was developed to identify patients with COVID-19. The experimental results showed that our model could accurately discriminate the COVID-19 patients from the bacteria pneumonia patients with an AUC of 0.95, recall (sensitivity) of 0.96, and precision of 0.79. When integrating three types of CT images, our model achieved a recall of 0.93 with precision of 0.86 for discriminating COVID-19 patients from others. Moreover, our model could extract main lesion features, especially the ground-glass opacity (GGO), which are visually helpful for assisted diagnoses by doctors. An online server is available for online diagnoses with CT images by our server . Source codes and datasets are available at our GitHub (GitHub).

COVID-19;Pneumonia;Syndrome

IEEE/ACM Trans Comput Biol Bioinform

Disease Outbreaks;Area under Curve

Self-recorded/clinical

2. Detection/Diagnosis

CT

34492574

10.1016/j.media.2021.102216

Yes

PMC8401374

2021-09-08

Journal Article;Multicenter Study

Peer reviewed (PubMed)

aiforcovid: predicting the clinical outcomes in patients with covid-19 applying ai to chest-x-rays an italian multicentre study

Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether artificial intelligence working with chest X-ray (CXR) scans and clinical data can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. Indeed, further to induce lower radiation dose than computed tomography (CT), CXR is a simpler and faster radiological technique, being also more widespread. In this respect, we present three approaches that use features extracted from CXR images, either handcrafted or automatically learnt by convolutional neuronal networks, which are then integrated with the clinical data. As a further contribution, this work introduces a repository that collects data from 820 patients enrolled in six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, suggesting that clinical data and images have the potential to provide useful information for the management of patients and hospital resources.

COVID-19;Death

Med Image Anal

Other Topics

Self-recorded/clinical

4. Prognosis/Treatment

X-Ray

32386147

10.1109/TMI.2020.2992546

Yes

2020-05-10

Journal Article

Peer reviewed (PubMed)

diagnosis of coronavirus disease 2019 (covid-19) with structured latent multi-view representation learning

Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of infected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting high-dimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the number of training data.

COVID-19;Pneumonia

IEEE Trans Med Imaging

Coronavirus Infections;Disease Outbreaks

Self-recorded/clinical

2. Detection/Diagnosis

CT

10.1101/2020.08.13.20174144

10.1101/2020.08.13.20174144

Yes

2020-08-14

Preprint

medRxiv

precise prediction of covid-19 in chest x-ray images using ke sieve algorithm

The novel coronavirus (COVID-19) pandemic is pressurizing the healthcare systems across the globe and few of them are on the verge of failing. The detection of this virus as early as possible will help in contaminating the spread of it as the virus is mutating itself as fast as possible and currently there are about 4,300 strains of the virus according to the reports. Clinical studies have shown that most of the COVID-19 patients suffer from a lung infection similar to influenza. So, it is possible to diagnose lung infection using imaging techniques. Although a chest computed tomography (CT) scan has been shown to be an effective imaging technique for lung-related disease diagnosis, chest X-ray is more widely available across the hospitals due to its considerably lower cost and faster imaging time than CT scan. The advancements in the area of machine learning and pattern recognition has resulted in intelligent systems that analyze CT Scans or X-ray images and classify between pneumonia and normal patients. This paper proposes KE Sieve Neural Network architecture, which helps in the rapid diagnosis of COVID-19 using chest X-ray images. This architecture is achieving an accuracy of 98.49%. This noninvasive prediction method can assist the doctors in this pandemic and reduce the stress on health care systems.

COVID-19;COVID-19 Pandemic;Infections;Influenza, Human;Pneumonia;Strains

Coronavirus Infections;Health Care

External

2. Detection/Diagnosis

X-Ray

33048773

10.1109/JBHI.2020.3030853

Yes

2020-10-14

Journal Article;Research Support, Non-U.S. Gov't

Peer reviewed (PubMed)

m 3lung-sys: a deep learning system for multi-class lung pneumonia screening from ct imaging

To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic. Considering the limited training cases and resources (e.g, time and budget), we propose a Multi-task Multi-slice Deep Learning System (M 3Lung-Sys) for multi-class lung pneumonia screening from CT imaging, which only consists of two 2D CNN networks, i.e., slice- and patient-level classification networks. The former aims to seek the feature representations from abundant CT slices instead of limited CT volumes, and for the overall pneumonia screening, the latter one could recover the temporal information by feature refinement and aggregation between different slices. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3Lung-Sys also be able to locate the areas of relevant lesions, without any pixel-level annotation. To further demonstrate the effectiveness of our model, we conduct extensive experiments on a chest CT imaging dataset with a total of 734 patients (251 healthy people, 245 COVID-19 patients, 105 H1N1 patients, and 133 CAP patients). The quantitative results with plenty of metrics indicate the superiority of our proposed model on both slice- and patient-level classification tasks. More importantly, the generated lesion location maps make our system interpretable and more valuable to clinicians.

COVID-19;Pneumonia

IEEE J Biomed Health Inform

Disease Outbreaks;Other Topics;Lung Diseases;Map;Cone-Beam Computed Tomography

Self-recorded/clinical

2. Detection/Diagnosis

CT

33065387

10.1016/j.compbiomed.2020.104037

Yes

PMC7543793

2020-10-17

Journal Article

Peer reviewed (PubMed)

multi-task deep learning based ct imaging analysis for covid-19 pneumonia: classification and segmentation

This paper presents an automatic classification segmentation tool for helping screening COVID-19 pneumonia using chest CT imaging. The segmented lesions can help to assess the severity of pneumonia and follow-up the patients. In this work, we propose a new multitask deep learning model to jointly identify COVID-19 patient and segment COVID-19 lesion from chest CT images. Three learning tasks: segmentation, classification and reconstruction are jointly performed with different datasets. Our motivation is on the one hand to leverage useful information contained in multiple related tasks to improve both segmentation and classification performances, and on the other hand to deal with the problems of small data because each task can have a relatively small dataset. Our architecture is composed of a common encoder for disentangled feature representation with three tasks, and two decoders and a multi-layer perceptron for reconstruction, segmentation and classification respectively. The proposed model is evaluated and compared with other image segmentation techniques using a dataset of 1369 patients including 449 patients with COVID-19, 425 normal ones, 98 with lung cancer and 397 of different kinds of pathology. The obtained results show very encouraging performance of our method with a dice coefficient higher than 0.88 for the segmentation and an area under the ROC curve higher than 97% for the classification.

COVID-19;Lung Cancer;Pneumonia

Comput Biol Med

Coronavirus Infections;Architecture;Neural Networks;ROC Curve

External

2. Detection/Diagnosis

CT

33449887

10.1109/JBHI.2021.3051470

Yes

2021-01-16

Journal Article

Peer reviewed (PubMed)

distant domain transfer learning for medical imaging

Medical image processing is one of the most important topics in the Internet of Medical Things (IoMT). Recently, deep learning methods have carried out state-of-the-art performances on medical imaging tasks. In this paper, we propose a novel transfer learning framework for medical image classification. Moreover, we apply our method COVID-19 diagnosis with lung Computed Tomography (CT) images. However, well-labeled training data sets cannot be easily accessed due to the disease's novelty and privacy policies. The proposed method has two components: reduced-size Unet Segmentation model and Distant Feature Fusion (DFF) classification model. This study is related to a not well-investigated but important transfer learning problem, termed Distant Domain Transfer Learning (DDTL). In this study, we develop a DDTL model for COVID-19 diagnosis using unlabeled Office-31, Caltech-256, and chest X-ray image data sets as the source data, and a small set of labeled COVID-19 lung CT as the target data. The main contributions of this study are: 1) the proposed method benefits from unlabeled data in distant domains which can be easily accessed, 2) it can effectively handle the distribution shift between the training data and the testing data, 3) it has achieved 96% classification accuracy, which is 13% higher classification accuracy than "non-transfer" algorithms, and 8% higher than existing transfer and distant transfer algorithms.

COVID-19

IEEE J Biomed Health Inform

Art;Transfer Learning;Algorithms;Lung;Image Processing;Tomography;Lung Diseases

External

Segmentation-only

CT

34192015

10.1136/bmjinnov-2020-000593

Yes

PMC7931213

2021-07-01

Journal Article

Peer reviewed (PubMed)

deep learning model to predict the need for mechanical ventilation using chest x-ray images in hospitalised patients with covid-19

There exists a wide gap in the availability of mechanical ventilator devices and their acute need in the context of the COVID-19 pandemic. An initial triaging method that accurately identifies the need for mechanical ventilation in hospitalised patients with COVID-19 is needed. We aimed to investigate if a potentially deteriorating clinical course in hospitalised patients with COVID-19 can be detected using all X-ray images taken during hospitalisation. We exploited the well-established DenseNet121 deep learning architecture for this purpose on 663 X-ray images acquired from 528 hospitalised patients with COVID-19. Two Pulmonary and Critical Care experts blindly and independently evaluated the same X-ray images for the purpose of validation. We found that our deep learning model predicted the need for mechanical ventilation with a high accuracy, sensitivity and specificity (90.06%, 86.34% and 84.38%, respectively). This prediction was done approximately 3 days ahead of the actual intubation event. Our model also outperformed two Pulmonary and Critical Care experts who evaluated the same X-ray images and provided an incremental accuracy of 7.24%-13.25%. Our deep learning model accurately predicted the need for mechanical ventilation early during hospitalisation of patients with COVID-19. Until effective preventive or treatment measures become widely available for patients with COVID-19, prognostic stratification as provided by our model is likely to be highly valuable.

COVID-19;COVID-19 Pandemic;Clinical Course

BMJ Innov

Other Topics

Self-recorded/clinical

4. Prognosis/Treatment

X-Ray

2007.15546

Yes

2022-01-10

Preprint

arXiv

comparative study of deep learning methods for the automatic segmentation of lung lesion and lesion type in ct scans of covid-19 patients

Recent research on COVID-19 suggests that CT imaging provides useful information to assess disease progression and assist diagnosis, in addition to help understanding the disease. There is an increasing number of studies that propose to use deep learning to provide fast and accurate quantification of COVID-19 using chest CT scans. The main tasks of interest are the automatic segmentation of lung and lung lesions in chest CT scans of confirmed or suspected COVID-19 patients. In this study, we compare twelve deep learning algorithms using a multi-center dataset, including both open-source and in-house developed algorithms. Results show that ensembling different methods can boost the overall test set performance for lung segmentation, binary lesion segmentation and multiclass lesion segmentation, resulting in mean Dice scores of 0.982, 0.724 and 0.469, respectively. The resulting binary lesions were segmented with a mean absolute volume error of 91.3 ml. In general, the task of distinguishing different lesion types was more difficult, with a mean absolute volume difference of 152 ml and mean Dice scores of 0.369 and 0.523 for consolidation and ground glass opacity, respectively. All methods perform binary lesion segmentation with an average volume error that is better than visual assessment by human raters, suggesting these methods are mature enough for a large-scale evaluation for use in clinical practice.

COVID-19;Disease Progression

Other Topics

Self-recorded/clinical

Segmentation-only

CT

34650825

Yes

PMC8513790

2021-10-16

Journal Article

Peer reviewed (PubMed)

context matters: graph-based self-supervised representation learning for medical images

Supervised learning method requires a large volume of annotated datasets. Collecting such datasets is time-consuming and expensive. Until now, very few annotated COVID-19 imaging datasets are available. Although self-supervised learning enables us to bootstrap the training by exploiting unlabeled data, the generic self-supervised methods for natural images do not sufficiently incorporate the context. For medical images, a desirable method should be sensitive enough to detect deviation from normal-appearing tissue of each anatomical region; here, anatomy is the context. We introduce a novel approach with two levels of self-supervised representation learning objectives: one on the regional anatomical level and another on the patient-level. We use graph neural networks to incorporate the relationship between different anatomical regions. The structure of the graph is informed by anatomical correspondences between each patient and an anatomical atlas. In addition, the graph representation has the advantage of handling any arbitrarily sized image in full resolution. Experiments on large-scale Computer Tomography (CT) datasets of lung images show that our approach compares favorably to baseline methods that do not account for the context. We use the learnt embedding to quantify the clinical progression of COVID-19 and show that our method generalizes well to COVID-19 patients from different hospitals. Qualitative results suggest that our model can identify clinically relevant regions in the images.

COVID-19;Clinical Course

Proc Conf AAAI Artif Intell

Other Topics

External

3. Monitoring/Severity assessment

CT

33044938

10.1109/JBHI.2020.3030224

Yes

2020-10-13

Journal Article;Research Support, N.I.H., Extramural

Peer reviewed (PubMed)

severity and consolidation quantification of covid-19 from ct images using deep learning based on hybrid weak labels

Early and accurate diagnosis of Coronavirus disease (COVID-19) is essential for patient isolation and contact tracing so that the spread of infection can be limited. Computed tomography (CT) can provide important information in COVID-19, especially for patients with moderate to severe disease as well as those with worsening cardiopulmonary status. As an automatic tool, deep learning methods can be utilized to perform semantic segmentation of affected lung regions, which is important to establish disease severity and prognosis prediction. Both the extent and type of pulmonary opacities help assess disease severity. However, manually pixel-level multi-class labelling is time-consuming, subjective, and non-quantitative. In this article, we proposed a hybrid weak label-based deep learning method that utilize both the manually annotated pulmonary opacities from COVID-19 pneumonia and the patient-level disease-type information available from the clinical report. A UNet was firstly trained with semantic labels to segment the total infected region. It was used to initialize another UNet, which was trained to segment the consolidations with patient-level information using the Expectation-Maximization (EM) algorithm. To demonstrate the performance of the proposed method, multi-institutional CT datasets from Iran, Italy, South Korea, and the United States were utilized. Results show that our proposed method can predict the infected regions as well as the consolidation regions with good correlation to human annotation.

COVID-19;Infections;Pneumonia

IEEE J Biomed Health Inform

Severity of Illness Index;Coronavirus Infections;Algorithms;Iran;Semantics;Report;Retrospective Studies

Self-recorded/clinical

3. Monitoring/Severity assessment

CT

2007.12525

10.3390/make2040027

Yes

2020-07-24

Preprint

arXiv

study of different deep learning approach with explainable ai for screening patients with covid-19 symptoms: using ct scan and chest x-ray image dataset

The outbreak of COVID-19 disease caused more than 100,000 deaths so far in the USA alone. It is necessary to conduct an initial screening of patients with the symptoms of COVID-19 disease to control the spread of the disease. However, it is becoming laborious to conduct the tests with the available testing kits due to the growing number of patients. Some studies proposed CT scan or chest X-ray images as an alternative solution. Therefore, it is essential to use every available resource, instead of either a CT scan or chest X-ray to conduct a large number of tests simultaneously. As a result, this study aims to develop a deep learning-based model that can detect COVID-19 patients with better accuracy both on CT scan and chest X-ray image dataset. In this work, eight different deep learning approaches such as VGG16, InceptionResNetV2, ResNet50, DenseNet201, VGG19, MobilenetV2, NasNetMobile, and ResNet15V2 have been tested on two dataset-one dataset includes 400 CT scan images, and another dataset includes 400 chest X-ray images studied. Besides, Local Interpretable Model-agnostic Explanations (LIME) is used to explain the model's interpretability. Using LIME, test results demonstrate that it is conceivable to interpret top features that should have worked to build a trust AI framework to distinguish between patients with COVID-19 symptoms with other patients.

COVID-19;Death

Disease Outbreaks;Other Topics

External

2. Detection/Diagnosis

Multimodal

32588200

10.1007/s13246-020-00888-x

Yes

PMC7315909

2020-06-27

Journal Article

Peer reviewed (PubMed)

truncated inception net: covid-19 outbreak screening using chest x-rays

Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in a short period of time, and the infection, caused by SARS-CoV-2, is spreading rapidly. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans and chest X-rays (CXRs). In this paper, motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems, a deep learning-based Convolutional Neural Network (CNN) model, which we call Truncated Inception Net, is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases. To validate our proposal, six different types of datasets were employed by taking the following CXRs: COVID-19 positive, Pneumonia positive, Tuberculosis positive, and healthy cases into account. The proposed model achieved an accuracy of 99.96% (AUC of 1.0) in classifying COVID-19 positive cases from combined Pneumonia and healthy cases. Similarly, it achieved an accuracy of 99.92% (AUC of 0.99) in classifying COVID-19 positive cases from combined Pneumonia, Tuberculosis, and healthy CXRs. To the best of our knowledge, as of now, the achieved results outperform the existing AI-driven tools for screening COVID-19 using the acquired CXRs, and proves the viability of using the proposed Truncated Inception Net as a screening tool.

COVID-19;COVID-19 Pandemic;Infections;Pneumonia;Tuberculosis

Phys Eng Sci Med

Coronavirus Infections;Disease Outbreaks;Neural Networks;ROC Curve;Lung Diseases;Area under Curve

External

2. Detection/Diagnosis

X-Ray

34891687

10.1109/EMBC46164.2021.9630945

Yes

2021-12-12

Journal Article;Research Support, Non-U.S. Gov't

Peer reviewed (PubMed)

interpreting uncertainty in model predictions for covid-19 diagnosis

COVID-19, due to its accelerated spread has brought in the need to use assistive tools for faster diagnosis in addition to typical lab swab testing. Chest X-Rays for COVID cases tend to show changes in the lungs such as ground glass opacities and peripheral consolidations which can be detected by deep neural networks. However, traditional convolutional networks use point estimate for predictions, lacking in capture of uncertainty, which makes them less reliable for adoption. There have been several works so far in predicting COVID positive cases with chest X-Rays. However, not much has been explored on quantifying the uncertainty of these predictions, interpreting uncertainty, and decomposing this to model or data uncertainty. To address these needs, we develop a visualization framework to address interpretability of uncertainty and its components, with uncertainty in predictions computed with a Bayesian Convolutional Neural Network. This framework aims to understand the contribution of individual features in the Chest-X-Ray images to predictive uncertainty. Providing this as an assistive tool can help the radiologist understand why the model came up with a prediction and whether the regions of interest captured by the model for the specific prediction are of significance in diagnosis. We demonstrate the usefulness of the tool in chest x-ray interpretation through several test cases from a benchmark dataset.

COVID-19

Annu Int Conf IEEE Eng Med Biol Soc

COVID-19 Testing;Other Topics

External

2. Detection/Diagnosis

X-Ray

2004.04582

Yes

2020-06-06

Preprint

arXiv

deepcovidexplainer: explainable covid-19 diagnosis based on chest x-ray images

Amid the coronavirus disease (COVID-19) pandemic, humanity experiences a rapid increase in infection numbers across the world. Challenge hospitals are faced with, in the fight against the virus, is the effective screening of incoming patients. One methodology is the assessment of chest radiography (CXR) images, which usually requires expert radiologist's knowledge. In this paper, we propose an explainable deep neural networks (DNN)-based method for automatic detection of COVID-19 symptoms from CXR images, which we call DeepCOVIDExplainer. We used 15,959 CXR images of 15,854 patients, covering normal, pneumonia, and COVID-19 cases. CXR images are first comprehensively preprocessed, before being augmented and classified with a neural ensemble method, followed by highlighting class-discriminating regions using gradient-guided class activation maps (Grad-CAM++) and layer-wise relevance propagation (LRP). Further, we provide human-interpretable explanations of the predictions. Evaluation results based on hold-out data show that our approach can identify COVID-19 confidently with a positive predictive value (PPV) of 91.6%, 92.45%, and 96.12%; precision, recall, and F1 score of 94.6%, 94.3%, and 94.6%, respectively for normal, pneumonia, and COVID-19 cases, respectively, making it comparable or improved results over recent approaches. We hope that our findings will be a useful contribution to the fight against COVID-19 and, in more general, towards an increasing acceptance and adoption of AI-assisted applications in the clinical practice.

COVID-19;COVID-19 Pandemic;Infections;Pneumonia

Coronavirus Infections;Map

External

2. Detection/Diagnosis

X-Ray

35110593

10.1038/s41598-022-05532-0

Yes

PMC8810911

2022-02-04

Journal Article;Research Support, N.I.H., Extramural;Research Support, Non-U.S. Gov't

Peer reviewed (PubMed)

effective deep learning approaches for predicting covid-19 outcomes from chest computed tomography volumes

The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from chest computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish COVID-19 pneumonia from chest CT volumes from healthy participants and participants with other viral pneumonia. Furthermore, we present a multitask model for joint segmentation of different classes of pulmonary lesions present in COVID-19 infected lungs that can outperform individual segmentation models for each task. We directly compare this multitask segmentation approach to combining feature-agnostic volumetric CT classification feature maps with clinical metadata for predicting mortality. We show that the combination of features derived from the chest CT volumes improve the AUC performance to 0.80 from the 0.52 obtained by using patients' clinical data alone. These approaches enable the automated extraction of clinically relevant features from chest CT volumes for risk stratification of COVID-19 patients.

COVID-19;COVID-19 Pandemic;Pneumonia;Pneumonia, Viral

Sci Rep

Image Processing;Lung Diseases;Area under Curve;Map;Cone-Beam Computed Tomography

External

4. Prognosis/Treatment

CT

2004.06689

Yes

2020-04-14

Preprint

arXiv

weakly supervised deep learning for covid-19 infection detection and classification from ct images

An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest of 13.04% in Algeria and 12.67% Italy (as of 8th April 2020). The onset of serious illness may result in death as a consequence of substantial alveolar damage and progressive respiratory failure. Although laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), is the golden standard for clinical diagnosis, the tests may produce false negatives. Moreover, under the pandemic situation, shortage of RT-PCR testing resources may also delay the following clinical decision and treatment. Under such circumstances, chest CT imaging has become a valuable tool for both diagnosis and prognosis of COVID-19 patients. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. The proposed method can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish COVID-19 from non-COVID-19 cases. Based on the promising results obtained qualitatively and quantitatively, we can envisage a wide deployment of our developed technique in large-scale clinical studies.

COVID-19;Death;Infections;Respiratory Failure

Coronavirus Infections;Disease Outbreaks;Polymerase Chain Reaction;Reverse Transcription

Self-recorded/clinical

2. Detection/Diagnosis

CT

32408222

10.1016/j.ejrad.2020.109041

Yes

PMC7198437

2020-05-15

Journal Article;Multicenter Study

Peer reviewed (PubMed)

deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicentre study

To develop a deep learning-based method to assist radiologists to fast and accurately identify patients with COVID-19 by CT images. We retrospectively collected chest CT images of 495 patients from three hospitals in China. 495 datasets were randomly divided into 395 cases (80%, 294 of COVID-19, 101 of other pneumonia) of the training set, 50 cases (10%, 37 of COVID-19, 13 of other pneumonia) of the validation set and 50 cases (10%, 37 of COVID-19, 13 of other pneumonia) of the testing set. We trained a multi-view fusion model using deep learning network to screen patients with COVID-19 using CT images with the maximum lung regions in axial, coronal and sagittal views. The performance of the proposed model was evaluated by both the validation and testing sets. The multi-view deep learning fusion model achieved the area under the receiver-operating characteristics curve (AUC) of 0.732, accuracy of 0.700, sensitivity of 0.730 and specificity of 0.615 in validation set. In the testing set, we can achieve AUC, accuracy, sensitivity and specificity of 0.819, 0.760, 0.811 and 0.615 respectively. Based on deep learning method, the proposed diagnosis model trained on multi-view images of chest CT images showed great potential to improve the efficacy of diagnosis and mitigate the heavy workload of radiologists for the initial screening of COVID-19 pneumonia.

COVID-19;Pneumonia

Eur J Radiol

Coronavirus Infections;Radiologists;ROC Curve;Retrospective Studies;Age;Receiver Operating Characteristic

Self-recorded/clinical

2. Detection/Diagnosis

CT

32427226

10.1016/j.chemolab.2020.104054

Yes

PMC7233238

2020-05-20

Journal Article

Peer reviewed (PubMed)

an automated residual exemplar local binary pattern and iterative relieff based covid-19 detection method using chest x-ray image

Coronavirus is normally transmitted from animal to person, but nowadays it is transmitted from person to person by changing its form. Covid-19 appeared as a very dangerous virus and unfortunately caused a worldwide pandemic disease. Radiology doctors use X-ray or CT images for the diagnosis of Covid-19. It has become crucial to help diagnose such images using image processing methods. Therefore, a novel intelligent computer vision method to automatically detect the Covid-19 virus was proposed. The proposed automatic Covid-19 detection method consists of preprocessing, feature extraction, and feature selection stages. Image resizing and grayscale conversion are used in the preprocessing phase. The proposed feature generation method is called Residual Exemplar Local Binary Pattern (ResExLBP). In the feature selection phase, a novel iterative ReliefF (IRF) based feature selection is used. Decision tree (DT), linear discriminant (LD), support vector machine (SVM), k nearest neighborhood (kNN), and subspace discriminant (SD) methods are chosen as classifiers in the classification phase. Leave one out cross-validation (LOOCV), 10-fold cross-validation, and holdout validation are used for training and testing. In this work, SVM classifier achieved 100.0% classification accuracy by using 10-fold cross-validation. This result clearly has shown that the perfect classification rate by using X-ray image for Covid-19 detection. The proposed ResExLBP and IRF based method is also cognitive, lightweight, and highly accurate.

COVID-19

Chemometr Intell Lab Syst

Viruses;Decision Trees

External

2. Detection/Diagnosis

X-Ray

2006.16106

10.1109/ICMLA51294.2020.00211

Yes

2020-10-20

Preprint

arXiv

covid-19 screening using residual attention network an artificial intelligence approach

Coronavirus Disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2). The virus transmits rapidly; it has a basic reproductive number R of 2.2-2.7. In March 2020, the World Health Organization declared the COVID-19 outbreak a pandemic. COVID-19 is currently affecting more than 200 countries with 6M active cases. An effective testing strategy for COVID-19 is crucial to controlling the outbreak but the demand for testing surpasses the availability of test kits that use Reverse Transcription Polymerase Chain Reaction (RT-PCR). In this paper, we present a technique to screen for COVID-19 using artificial intelligence. Our technique takes only seconds to screen for the presence of the virus in a patient. We collected a dataset of chest X-ray images and trained several popular deep convolution neural network-based models (VGG, MobileNet, Xception, DenseNet, InceptionResNet) to classify the chest X-rays. Unsatisfied with these models, we then designed and built a Residual Attention Network that was able to screen COVID-19 with a testing accuracy of 98% and a validation accuracy of 100%. A feature maps visual of our model show areas in a chest X-ray which are important for classification. Our work can help to increase the adaptation of AI-assisted applications in clinical practice. The code and dataset used in this project are available at GitHub

COVID-19;COVID-19 Pandemic;Severe Acute Respiratory Syndrome

World Health Organization;Disease Outbreaks;Health;Polymerase Chain Reaction;Map;Reverse Transcription

External

2. Detection/Diagnosis

X-Ray

33810066

10.3390/s21062215

Yes

PMC8004971

2021-04-04

Journal Article

Peer reviewed (PubMed)

a few-shot u-net deep learning model for covid-19 infected area segmentation in ct images

Recent studies indicate that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 identification. In this paper, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia-infected area segmentation in CT images for the detection of COVID-19. Traditional methods for CT scan segmentation exploit a supervised learning paradigm, so they require large volumes of data for their training, and assume fixed (static) network weights once the training procedure has been completed. Recently, to overcome these difficulties, few-shot learning (FSL) has been introduced as a general concept of network model training using a very small amount of samples. In this paper, we explore the efficacy of few-shot learning in U-Net architectures, allowing for a dynamic fine-tuning of the network weights as new few samples are being fed into the U-Net. Experimental results indicate improvement in the segmentation accuracy of identifying COVID-19 infected regions. In particular, using 4-fold cross-validation results of the different classifiers, we observed an improvement of 5.388 % for all test data regarding the IoU metric and a similar increment of 5.394 % for the F1 score. Moreover, the statistical significance of the improvement obtained using our proposed few-shot U-Net architecture compared with the traditional U-Net model was confirmed by applying the Kruskal-Wallis test (p-value = 0.026).

COVID-19;Pneumonia

Sensors (Basel)

Semantics;Sensitivity and Specificity

External

Segmentation-only

CT

32730216

10.1109/TMI.2020.2995108

Yes

PMC7393217

2020-07-31

Journal Article;Research Support, N.I.H., Extramural

Peer reviewed (PubMed)

relational modeling for robust and efficient pulmonary lobe segmentation in ct scans

Pulmonary lobe segmentation in computed tomography scans is essential for regional assessment of pulmonary diseases. Recent works based on convolution neural networks have achieved good performance for this task. However, they are still limited in capturing structured relationships due to the nature of convolution. The shape of the pulmonary lobes affect each other and their borders relate to the appearance of other structures, such as vessels, airways, and the pleural wall. We argue that such structural relationships play a critical role in the accurate delineation of pulmonary lobes when the lungs are affected by diseases such as COVID-19 or COPD. In this paper, we propose a relational approach (RTSU-Net) that leverages structured relationships by introducing a novel non-local neural network module. The proposed module learns both visual and geometric relationships among all convolution features to produce self-attention weights. With a limited amount of training data available from COVID-19 subjects, we initially train and validate RTSU-Net on a cohort of 5000 subjects from the COPDGene study (4000 for training and 1000 for evaluation). Using models pre-trained on COPDGene, we apply transfer learning to retrain and evaluate RTSU-Net on 470 COVID-19 suspects (370 for retraining and 100 for evaluation). Experimental results show that RTSU-Net outperforms three baselines and performs robustly on cases with severe lung infection due to COVID-19.

COVID-19;Infections;Lung Diseases;Pulmonary Disease, Chronic Obstructive

IEEE Trans Med Imaging

Coronavirus Infections;Transfer Learning;Algorithms;Lung;Neural Networks;Tomography

Self-recorded/clinical

Segmentation-only

CT

10.1101/2020.05.26.20113761

10.1101/2020.05.26.20113761

Yes

2020-05-27

Preprint

medRxiv

differentiating covid-19 from other types of pneumonia with convolutional neural networks

A widely-used method for diagnosing COVID-19 is the nucleic acid test based on real-time reverse transcriptase-polymerase chain reaction (RT-PCR). However, the sensitivity of real time RT-PCR tests is low and it can take up to 8 hours to receive the test results. Radiologic methods can provide higher sensitivity. The aim of this study is to investigate the use of X-ray and convolutional neural networks for the diagnosis of COVID-19 and to differentiate it from viral and/or bacterial pneumonia, as 2-class (bacterial pneumonia vs COVID-19 and viral pneumonia vs COVID-19) and 3- class (bacterial pneumonia, COVID-19, and healthy group (BCH), and among viral pneumonia, COVID- 19, and healthy group (VCH)) experiments. 225 COVID-19, 1,583 healthy control, 2,780 bacterial pneumonia, and 1,493 viral pneumonia chest X-ray images were used. 2-class- and 3-class-experiments were performed with different convolutional neural network (ConvNet) architectures, with different variations of convolutional layers and fully-connected layers. The results showed that bacterial pneumonia vs COVID-19 and viral pneumonia vs COVID- 19 reached a mean ROC AUC of 97.32% and 96.80%, respectively. In the 3-class-experiments, macro-average F1 scores of 95.79% and 94.59% were obtained in terms of detecting COVID-19 among BCH and VCH, respectively. The ConvNet was able to distinguish the COVID-19 images among non-COVID-19 images, namely bacterial and viral pneumonia as well as normal X-ray images.

COVID-19;Pneumonia;Pneumonia, Bacterial;Pneumonia, Viral

Polymerase Chain Reaction;Area under Curve;Nucleic Acids

External

2. Detection/Diagnosis

X-Ray

2009.09725

Yes

2020-09-21

Preprint

arXiv

improving automated covid-19 grading with convolutional neural networks in computed tomography scans: an ablation study

Amidst the ongoing pandemic, several studies have shown that COVID-19 classification and grading using computed tomography (CT) images can be automated with convolutional neural networks (CNNs). Many of these studies focused on reporting initial results of algorithms that were assembled from commonly used components. The choice of these components was often pragmatic rather than systematic. For instance, several studies used 2D CNNs even though these might not be optimal for handling 3D CT volumes. This paper identifies a variety of components that increase the performance of CNN-based algorithms for COVID-19 grading from CT images. We investigated the effectiveness of using a 3D CNN instead of a 2D CNN, of using transfer learning to initialize the network, of providing automatically computed lesion maps as additional network input, and of predicting a continuous instead of a categorical output. A 3D CNN with these components achieved an area under the ROC curve (AUC) of 0.934 on our test set of 105 CT scans and an AUC of 0.923 on a publicly available set of 742 CT scans, a substantial improvement in comparison with a previously published 2D CNN. An ablation study demonstrated that in addition to using a 3D CNN instead of a 2D CNN transfer learning contributed the most and continuous output contributed the least to improving the model performance.

COVID-19

Transfer Learning;Algorithms;Tomography;ROC Curve;Area under Curve;Map;Cone-Beam Computed Tomography

Self-recorded/clinical

2. Detection/Diagnosis

Multimodal

10.1101/2020.08.13.20173997

10.1101/2020.08.13.20173997

Yes

2020-08-14

Preprint

medRxiv

deep learning for automated recognition of covid-19 from chest x-ray images

The pandemic caused by coronavirus in recent months is having a devastating global effect, which puts the world under the most ever unprecedented emergency. Currently, since there are not effective antiviral treatments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thus helping to reduce mortality. While a corresponding vaccine is being developed, and different measures are being used to combat the virus, medical imaging techniques have also been investigated to assist doctors in diagnosing this disease.This paper presents a practical solution for the detection of Covid-19 from chest X-ray (CXR) images, exploiting cutting-edge Machine Learning techniques.We employ EfficientNet and MixNet, two recently developed families of deep neural networks, as the main classification engine. Furthermore, we also apply different transfer learning strategies, aiming at making the training process more accurate and efficient. The proposed approach has been validated by means of two real datasets, the former consists of 13,511 training images and 1,489 testing images, the latter has 14,324 and 3,581 images for training and testing, respectively.The results are promising: by all the experimental configurations considered in the evaluation, our approach always yields an accuracy larger than 95.0%, with the maximum accuracy obtained being 96.64%.As a comparison with various existing studies, we can thus conclude that our performance improvement is significant.

COVID-19

Transfer Learning;Antiviral Agents;Pharmaceutical Preparations

External

2. Detection/Diagnosis

X-Ray

34853342

10.1038/s41598-021-02003-w

Yes

PMC8636645

2021-12-03

Journal Article;Research Support, Non-U.S. Gov't;Validation Study

Peer reviewed (PubMed)

validation of expert system enhanced deep learning algorithm for automated screening for covid-pneumonia on chest x-rays

SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66-0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral.

COVID-19;Pneumonia

Sci Rep

Predictive Value;Image Processing;Polymerase Chain Reaction;Neural Networks;Radiologists;Retrospective Studies

Self-recorded/clinical

2. Detection/Diagnosis

X-Ray

2006.14419

Yes

2020-06-26

Preprint

arXiv

a novel and reliable deep learning web-based tool to detect covid-19 infection from chest ct-scan

The corona virus is already spread around the world in many countries, and it has taken many lives. Furthermore, the world health organization (WHO) has announced that COVID-19 has reached the global epidemic stage. Early and reliable diagnosis using chest CT-scan can assist medical specialists in vital circumstances. In this work, we introduce a computer aided diagnosis (CAD) web service to detect COVID- 19 online. One of the largest public chest CT-scan databases, containing 746 participants was used in this experiment. A number of well-known deep neural network architectures consisting of ResNet, Inception and MobileNet were inspected to find the most efficient model for the hybrid system. A combination of the Densely connected convolutional network (DenseNet) in order to reduce image dimensions and Nu-SVM as an anti-overfitting bottleneck was chosen to distinguish between COVID-19 and healthy controls. The proposed methodology achieved 90.80% recall, 89.76% precision and 90.61% accuracy. The method also yields an AUC of 95.05%. Ultimately a flask web service is made public through ngrok using the trained models to provide a RESTful COVID-19 detector, which takes only 39 milliseconds to process one image. The source code is also available at GitHub Based on the findings, it can be inferred that it is feasible to use the proposed technique as an automated tool for diagnosis of COVID-19.

COVID-19;Infections

Other Topics

External

2. Detection/Diagnosis

CT

34976571

10.1109/ACCESS.2021.3058537

Yes

PMC8675557

2022-01-04

Journal Article

Peer reviewed (PubMed)

a review on deep learning techniques for the diagnosis of novel coronavirus (covid-19)

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. The aim of this paper is to facilitate experts (medical or otherwise) and technicians in understanding the ways deep learning techniques are used in this regard and how they can be potentially further utilized to combat the outbreak of COVID-19.

COVID-19

IEEE Access

Transfer Learning;Research Personnel;Disease Outbreaks;Tomography

N.A.

Review

Multimodal

32787937

10.1186/s12938-020-00807-x

Yes

PMC7422684

2020-08-14

Journal Article

Peer reviewed (PubMed)

rapid identification of covid-19 severity in ct scans through classification of deep features

Chest CT is used for the assessment of the severity of patients infected with novel coronavirus 2019 (COVID-19). We collected chest CT scans of 202 patients diagnosed with the COVID-19, and try to develop a rapid, accurate and automatic tool for severity screening follow-up therapeutic treatment. A total of 729 2D axial plan slices with 246 severe cases and 483 non-severe cases were employed in this study. By taking the advantages of the pre-trained deep neural network, four pre-trained off-the-shelf deep models (Inception-V3, ResNet-50, ResNet-101, DenseNet-201) were exploited to extract the features from these CT scans. These features are then fed to multiple classifiers (linear discriminant, linear SVM, cubic SVM, KNN and Adaboost decision tree) to identify the severe and non-severe COVID-19 cases. Three validation strategies (holdout validation, tenfold cross-validation and leave-one-out) are employed to validate the feasibility of proposed pipelines. The experimental results demonstrate that classification of the features from pre-trained deep models shows the promising application in COVID-19 severity screening, whereas the DenseNet-201 with cubic SVM model achieved the best performance. Specifically, it achieved the highest severity classification accuracy of 95.20% and 95.34% for tenfold cross-validation and leave-one-out, respectively. The established pipeline was able to achieve a rapid and accurate identification of the severity of COVID-19. This may assist the physicians to make more efficient and reliable decisions.

COVID-19

Biomed Eng Online

Pneumonia;Decision Trees

External

3. Monitoring/Severity assessment

CT

34539221

10.1007/s11042-021-11299-9

Yes

PMC8436200

2021-09-21

Journal Article

Peer reviewed (PubMed)

automatic deep learning system for covid-19 infection quantification in chest ct

The paper proposes an automatic deep learning system for COVID-19 infection areas segmentation in chest CT scans. CT imaging proved its ability to detect the COVID-19 disease even for asymptotic patients, which make it a trustworthy alternative for PCR. Coronavirus disease spread globally and PCR screening is the adopted diagnostic testing method for COVID-19 detection. However, PCR is criticized due its low sensitivity ratios, also, it is time-consuming and manual complicated process. The proposed framework includes different steps; it starts to prepare the region of interest by segmenting the lung organ, which then undergoes edge enhancing diffusion filtering (EED) to improve the infection areas contrast and intensity homogeneity. The proposed FCN is implemented using U-net architecture with modified residual block to include concatenation skip connection. The block improves the learning of gradient values by forwarding the infection area features through the network. The proposed system is evaluated using different measures and achieved dice overlapping score of 0.961 and 0.780 for lung and infection areas segmentation, respectively. The proposed system is trained and tested using many 2D CT slices extracted from diverse datasets from different sources, which demonstrate the system generalization and effectiveness. The use of more datasets from different sources helps to enhance the system accuracy and generalization, which can be accomplished based on the data availability in in the future.

COVID-19;Infections;Postoperative Residual Curarization

Multimed Tools Appl

Coronavirus Infections;Polymerase Chain Reaction

External

Segmentation-only

CT

32412551

10.1007/s40846-020-00529-4

Yes

PMC7221329

2020-05-16

Journal Article

Peer reviewed (PubMed)

extracting possibly representative covid-19 biomarkers from x-ray images with deep learning approach and image data related to pulmonary diseases

While the spread of COVID-19 is increased, new, automatic, and reliable methods for accurate detection are essential to reduce the exposure of the medical experts to the outbreak. X-ray imaging, although limited to specific visualizations, may be helpful for the diagnosis. In this study, the problem of automatic classification of pulmonary diseases, including the recently emerged COVID-19, from X-ray images, is considered. Deep Learning has proven to be a remarkable method to extract massive high-dimensional features from medical images. Specifically, in this paper, the state-of-the-art Convolutional Neural Network called Mobile Net is employed and trained from scratch to investigate the importance of the extracted features for the classification task. A large-scale dataset of 3905 X-ray images, corresponding to 6 diseases, is utilized for training MobileNet v2, which has been proven to achieve excellent results in related tasks. Training the CNNs from scratch outperforms the other transfer learning techniques, both in distinguishing the X-rays between the seven classes and between Covid-19 and non-Covid-19. A classification accuracy between the seven classes of 87.66% is achieved. Besides, this method achieves 99.18% accuracy, 97.36% Sensitivity, and 99.42% Specificity in the detection of COVID-19. The results suggest that training CNNs from scratch may reveal vital biomarkers related but not limited to the COVID-19 disease, while the top classification accuracy suggests further examination of the X-ray imaging potential.

COVID-19;Lung Diseases

J Med Biol Eng

Art;Transfer Learning;Disease Outbreaks;Lung;Other Topics;Lung Diseases

External

2. Detection/Diagnosis

X-Ray

2003.05037

Yes

2020-03-24

Preprint

arXiv

rapid ai development cycle for the coronavirus (covid-19) pandemic: initial results for automated detection and patient monitoring using deep learning ct image analysis

Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus; demonstrate they can differentiate coronavirus patients from non-patients. : Multiple international datasets, including from Chinese disease-infected areas were included. We present a system that utilizes robust 2D and 3D deep learning models, modifying and adapting existing AI models and combining them with clinical understanding. We conducted multiple retrospective experiments to analyze the performance of the system in the detection of suspected COVID-19 thoracic CT features and to evaluate evolution of the disease in each patient over time using a 3D volume review, generating a Corona score. The study includes a testing set of 157 international patients (China and U.S). Classification results for Coronavirus vs Non-coronavirus cases per thoracic CT studies were 0.996 AUC (95%CI: 0.989-1.00) ; on datasets of Chinese control and infected patients. 98.2% sensitivity, 92.2% specificity. For time analysis of Coronavirus patients, the system output enables quantitative measurements for smaller opacities (volume, diameter) and visualization of the larger opacities in a slice-based heat map or a 3D volume display. Our suggested Corona score measures the progression of disease over time. This initial study, which is currently being expanded to a larger population, demonstrated that rapidly developed AI-based image analysis can achieve high accuracy in detection of Coronavirus as well as quantification and tracking of disease burden.

COVID-19;COVID-19 Pandemic

Specificity;Area under Curve;Map

Self-recorded/clinical

3. Monitoring/Severity assessment

CT

33042210

10.1016/j.bspc.2020.102257

Yes

PMC7538100

2020-10-13

Journal Article

Peer reviewed (PubMed)

mh-covidnet: diagnosis of covid-19 using deep neural networks and meta-heuristic-based feature selection on x-ray images

COVID-19 is a disease that causes symptoms in the lungs and causes deaths around the world. Studies are ongoing for the diagnosis and treatment of this disease, which is defined as a pandemic. Early diagnosis of this disease is important for human life. This process is progressing rapidly with diagnostic studies based on deep learning. Therefore, to contribute to this field, a deep learning-based approach that can be used for early diagnosis of the disease is proposed in our study. In this approach, a data set consisting of 3 classes of COVID19, normal and pneumonia lung X-ray images was created, with each class containing 364 images. Pre-processing was performed using the image contrast enhancement algorithm on the prepared data set and a new data set was obtained. Feature extraction was completed from this data set with deep learning models such as AlexNet, VGG19, GoogleNet, and ResNet. For the selection of the best potential features, two metaheuristic algorithms of binary particle swarm optimization and binary gray wolf optimization were used. After combining the features obtained in the feature selection of the enhancement data set, they were classified using SVM. The overall accuracy of the proposed approach was obtained as 99.38%. The results obtained by verification with two different metaheuristic algorithms proved that the approach we propose can help experts during COVID-19 diagnostic studies.

COVID-19;Death;Pneumonia

Biomed Signal Process Control

Other Topics

External

2. Detection/Diagnosis

X-Ray

32750973

10.1109/JBHI.2020.3012383

Yes

PMC8545159

2020-08-06

Journal Article;Research Support, Non-U.S. Gov't;Validation Study

Peer reviewed (PubMed)

introducing the gev activation function for highly unbalanced data to develop covid-19 diagnostic models

Fast and accurate diagnosis is essential for the efficient and effective control of the COVID-19 pandemic that is currently disrupting the whole world. Despite the prevalence of the COVID-19 outbreak, relatively few diagnostic images are openly available to develop automatic diagnosis algorithms. Traditional deep learning methods often struggle when data is highly unbalanced with many cases in one class and only a few cases in another; new methods must be developed to overcome this challenge. We propose a novel activation function based on the generalized extreme value (GEV) distribution from extreme value theory, which improves performance over the traditional sigmoid activation function when one class significantly outweighs the other. We demonstrate the proposed activation function on a publicly available dataset and externally validate on a dataset consisting of 1,909 healthy chest X-rays and 84 COVID-19 X-rays. The proposed method achieves an improved area under the receiver operating characteristic (DeLong's p-value < 0.05) compared to the sigmoid activation. Our method is also demonstrated on a dataset of healthy and pneumonia vs. COVID-19 X-rays and a set of computerized tomography images, achieving improved sensitivity. The proposed GEV activation function significantly improves upon the previously used sigmoid activation for binary classification. This new paradigm is expected to play a significant role in the fight against COVID-19 and other diseases, with relatively few training cases available.

COVID-19;COVID-19 Pandemic;Pneumonia

IEEE J Biomed Health Inform

Coronavirus Infections;Disease Outbreaks;COVID-19 Testing;Neural Networks

External

2. Detection/Diagnosis

Multimodal

2008.09713

Yes

2020-08-21

Preprint

arXiv

comparative performance analysis of the resnet backbones of mask rcnn to segment the signs of covid-19 in chest ct scans

COVID-19 has been detrimental in terms of the number of fatalities and rising number of critical patients across the world. According to the UNDP (United National Development Programme) Socio-Economic programme, aimed at the COVID-19 crisis, the pandemic is far more than a health crisis: it is affecting societies and economies at their core. There has been greater developments recently in the chest X-ray-based imaging technique as part of the COVID-19 diagnosis especially using Convolution Neural Networks (CNN) for recognising and classifying images. However, given the limitation of supervised labelled imaging data, the classification and predictive risk modelling of medical diagnosis tend to compromise. This paper aims to identify and monitor the effects of COVID-19 on the human lungs by employing Deep Neural Networks on axial CT (Chest Computed Tomography) scan of lungs. We have adopted Mask RCNN, with ResNet50 and ResNet101 as its backbone, to segment the regions, affected by COVID-19 coronavirus. Using the regions of human lungs, where symptoms have manifested, the model classifies condition of the patient as either "Mild" or "Alarming". Moreover, the model is deployed on the Google Cloud Platform (GCP) to simulate the online usage of the model for performance evaluation and accuracy improvement. The ResNet101 backbone model produces an F1 score of 0.85 and faster prediction scores with an average time of 9.04 seconds per inference.

COVID-19

Other Topics

External

2. Detection/Diagnosis

CT

32989379

10.1016/j.asoc.2020.106744

Yes

PMC7510455

2020-09-30

Journal Article

Peer reviewed (PubMed)

learning distinctive filters for covid-19 detection from chest x-ray using shuffled residual cnn

COVID-19 is a deadly viral infection that has brought a significant threat to human lives. Automatic diagnosis of COVID-19 from medical imaging enables precise medication, helps to control community outbreak, and reinforces coronavirus testing methods in place. While there exist several challenges in manually inferring traces of this viral infection from X-ray, Convolutional Neural Network (CNN) can mine data patterns that capture subtle distinctions between infected and normal X-rays. To enable automated learning of such latent features, a custom CNN architecture has been proposed in this research. It learns unique convolutional filter patterns for each kind of pneumonia. This is achieved by restricting certain filters in a convolutional layer to maximally respond only to a particular class of pneumonia/COVID-19. The CNN architecture integrates different convolution types to aid better context for learning robust features and strengthen gradient flow between layers. The proposed work also visualizes regions of saliency on the X-ray that have had the most influence on CNN's prediction outcome. To the best of our knowledge, this is the first attempt in deep learning to learn custom filters within a single convolutional layer for identifying specific pneumonia classes. Experimental results demonstrate that the proposed work has significant potential in augmenting current testing methods for COVID-19. It achieves an F1-score of 97.20% and an accuracy of 99.80% on the COVID-19 X-ray set.

COVID-19;Pneumonia;Virus Diseases

Appl Soft Comput

Architecture;Disease Outbreaks

External

2. Detection/Diagnosis

X-Ray

34101042

10.1007/s10916-021-01745-4

Yes

PMC8185498

2021-06-09

Journal Article

Peer reviewed (PubMed)

deep learning on chest x-ray images to detect and evaluate pneumonia cases at the era of covid-19

Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. COVID-19 appeared first in China and very quickly spreads to the rest of the world, causing then the 2019-20 coronavirus pandemic. In many cases, this disease causes pneumonia. Since pulmonary infections can be observed through radiography images, this paper investigates deep learning methods for automatically analyzing query chest X-ray images with the hope to bring precision tools to health professionals towards screening the COVID-19 and diagnosing confirmed patients. In this context, training datasets, deep learning architectures and analysis strategies have been experimented from publicly open sets of chest X-ray images. Tailored deep learning models are proposed to detect pneumonia infection cases, notably viral cases. It is assumed that viral pneumonia cases detected during an epidemic COVID-19 context have a high probability to presume COVID-19 infections. Moreover, easy-to-apply health indicators are proposed for estimating infection status and predicting patient status from the detected pneumonia cases. Experimental results show possibilities of training deep learning models over publicly open sets of chest X-ray images towards screening viral pneumonia. Chest X-ray test images of COVID-19 infected patients are successfully diagnosed through detection models retained for their performances. The efficiency of proposed health indicators is highlighted through simulated scenarios of patients presenting infections and health problems by combining real and synthetic health data.

COVID-19;Communicable Diseases;Infections;Pneumonia;Pneumonia, Viral

J Med Syst

Coronavirus Infections;Architecture;Neural Networks;Communicable Diseases

External

2. Detection/Diagnosis

X-Ray

33199977

10.1016/j.asoc.2020.106897

Yes

PMC7654325

2020-11-18

Journal Article

Peer reviewed (PubMed)

ai-assisted ct imaging analysis for covid-19 screening: building and deploying a medical ai system

The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hope artificial intelligence (AI) to reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. In this paper, we present our experience in building and deploying an AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia. The proposed system which consists of classification and segmentation will save about 30%-40% of the detection time for physicians and promote the performance of COVID-19 detection. Specifically, working in an interdisciplinary team of over 30 people with medical and/or AI background, geographically distributed in Beijing and Wuhan, we are able to overcome a series of challenges (e.g. data discrepancy, testing time-effectiveness of model, data security, etc.) in this particular situation and deploy the system in four weeks. In addition, since the proposed AI system provides the priority of each CT image with probability of infection, the physicians can confirm and segregate the infected patients in time. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we are able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases.

COVID-19;Infections;Lung Diseases;Pneumonia

Appl Soft Comput

Disease Outbreaks;Radiologists

Self-recorded/clinical

2. Detection/Diagnosis

CT

2009.05096

Yes

2020-09-10

Preprint

arXiv

covid ct-net: predicting covid-19 from chest ct images using attentional convolutional network

The novel corona-virus disease (COVID-19) pandemic has caused a major outbreak in more than 200 countries around the world, leading to a severe impact on the health and life of many people globally. As of Aug 25th of 2020, more than 20 million people are infected, and more than 800,000 death are reported. Computed Tomography (CT) images can be used as a as an alternative to the time-consuming "reverse transcription polymerase chain reaction (RT-PCR)" test, to detect COVID-19. In this work we developed a deep learning framework to predict COVID-19 from CT images. We propose to use an attentional convolution network, which can focus on the infected areas of chest, enabling it to perform a more accurate prediction. We trained our model on a dataset of more than 2000 CT images, and report its performance in terms of various popular metrics, such as sensitivity, specificity, area under the curve, and also precision-recall curve, and achieve very promising results. We also provide a visualization of the attention maps of the model for several test images, and show that our model is attending to the infected regions as intended. In addition to developing a machine learning modeling framework, we also provide the manual annotation of the potentionally infected regions of chest, with the help of a board-certified radiologist, and make that publicly available for other researchers.

COVID-19;Death

Specificity;Research Personnel;Disease Outbreaks;Health;Polymerase Chain Reaction;Radiologists;Map;Reverse Transcription

Self-recorded/clinical

2. Detection/Diagnosis

CT

2012.10787

Yes

2021-02-12

Preprint

arXiv

constructing and evaluating an explainable model for covid-19 diagnosis from chest x-rays

In this paper, our focus is on constructing models to assist a clinician in the diagnosis of COVID-19 patients in situations where it is easier and cheaper to obtain X-ray data than to obtain high-quality images like those from CT scans. Deep neural networks have repeatedly been shown to be capable of constructing highly predictive models for disease detection directly from image data. However, their use in assisting clinicians has repeatedly hit a stumbling block due to their black-box nature. Some of this difficulty can be alleviated if predictions were accompanied by explanations expressed in clinically relevant terms. In this paper, deep neural networks are used to extract domain-specific features(morphological features like ground-glass opacity and disease indications like pneumonia) directly from the image data. Predictions about these features are then used to construct a symbolic model (a decision tree) for the diagnosis of COVID-19 from chest X-rays, accompanied with two kinds of explanations: visual (saliency maps, derived from the neural stage), and textual (logical descriptions, derived from the symbolic stage). A radiologist rates the usefulness of the visual and textual explanations. Our results demonstrate that neural models can be employed usefully in identifying domain-specific features from low-level image data; that textual explanations in terms of clinically relevant features may be useful; and that visual explanations will need to be clinically meaningful to be useful.

COVID-19;Pneumonia

Pneumonia;Other Topics;Decision Trees;Map

External

2. Detection/Diagnosis

X-Ray

34786295

10.1109/ACCESS.2020.3033762

Yes

PMC8545263

2021-11-18

Journal Article

Peer reviewed (PubMed)

deep convolutional approaches for the analysis of covid-19 using chest x-ray images from portable devices

The recent human coronavirus disease (COVID-19) is a respiratory infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the effects of COVID-19 in pulmonary tissues, chest radiography imaging plays an important role in the screening, early detection, and monitoring of the suspected individuals. Hence, as the pandemic of COVID-19 progresses, there will be a greater reliance on the use of portable equipment for the acquisition of chest X-ray images due to its accessibility, widespread availability, and benefits regarding to infection control issues, minimizing the risk of cross-contamination. This work presents novel fully automatic approaches specifically tailored for the classification of chest X-ray images acquired by portable equipment into 3 different clinical categories: normal, pathological, and COVID-19. For this purpose, 3 complementary deep learning approaches based on a densely convolutional network architecture are herein presented. The joint response of all the approaches allows to enhance the differentiation between patients infected with COVID-19, patients with other diseases that manifest characteristics similar to COVID-19 and normal cases. The proposed approaches were validated over a dataset specifically retrieved for this research. Despite the poor quality of the chest X-ray images that is inherent to the nature of the portable equipment, the proposed approaches provided global accuracy values of 79.62%, 90.27% and 79.86%, respectively, allowing a reliable analysis of portable radiographs to facilitate the clinical decision-making process.

COVID-19;Infections;Respiratory Tract Infections;Severe Acute Respiratory Syndrome

IEEE Access

Other Topics

Self-recorded/clinical

2. Detection/Diagnosis

X-Ray

32834634

10.1016/j.chaos.2020.110122

Yes

PMC7357532

2020-08-25