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| <title><strong>HAM10000 Classification</strong></title> | |
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| <h1 class="title"><strong>HAM10000 Classification</strong></h1> | |
| <h2 class="subtitle"><strong>Kalbe Digital Lab</strong></h2> | |
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| <h3 class="overview-heading"><span class="vl">Overview</span></h3> | |
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| The HAM10000 Skin Cancer Classification program serves the critical purpose of categorizing skin lesions into seven distinct classes: Actinic Keratosis, Basal Cell Carcinoma, Benign Keratosis, Dermatofibroma, Melanoma, Melanocytic Nevi, and Vascular Lesion. This program is trained using a ResNet18 model. | |
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| <h3 class="overview-heading"><span class="vl">Dataset</span></h3> | |
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| The program has been meticulously trained on a robust and diverse dataset, specifically <a href="https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000" target="_blank">Skin Cancer : HAM10000 Dataset.</a>. | |
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| The program has been meticulously trained on a diverse and extensive dataset, known as the HAM10000 dataset. This dataset consists of 10,015 dermatoscopic images of skin lesions, meticulously collected and manually annotated. It is a valuable resource that has played a pivotal role in training our skin cancer classification model. | |
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| <ul> | |
| <li>Objective: HAM 10000 Identification</li> | |
| <li>Task: Classification</li> | |
| <li>Modality: Colour Images</li> | |
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| <h3 class="overview-heading"><span class="vl">Model Architecture</span></h3> | |
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| The model architecture of ResNet18 to train images for classifying skin cancer part. | |
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| <img class="content-image" src="file/figures/ResNet-18.png" alt="model-architecture" width="425" height="115" style="vertical-align:middle" /> | |
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| <h3 class="overview-heading"><span class="vl">Demo</span></h3> | |
| <p class="overview-content">Please select or upload a skin cancer scan image to see the capabilities of skin part classification with this model</p> | |
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