acevedo

Dataset Description

Source: https://www.sciencedirect.com/science/article/pii/S2352340920303681 Source Data: https://data.mendeley.com/datasets/snkd93bnjr/1

Dataset Structure

Data Fields

• image: PIL Image
• label: Integer label
• metadata: Dictionary of metadata

Data Splits

Split	Size
train	17092
train	17092

Usage

from datasets import load_dataset

dataset = load_dataset("digipath/acevedo")

License

CC-BY-4.0

Citation

If you use this dataset, please cite:

@article{acevedo_dataset_2020,
    title = {A dataset of microscopic peripheral blood cell images for development of automatic recognition systems},
    volume = {30},
    issn = {2352-3409},
    url = {https://www.sciencedirect.com/science/article/pii/S2352340920303681},
    doi = {10.1016/j.dib.2020.105474},
    abstract = {This article makes available a dataset that was used for the development of an automatic recognition system of peripheral blood cell images using convolutional neural networks [1]. The dataset contains a total of 17,092 images of individual normal cells, which were acquired using the analyzer CellaVision DM96 in the Core Laboratory at the Hospital Clinic of Barcelona. The dataset is organized in the following eight groups: neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (promyelocytes, myelocytes, and metamyelocytes), erythroblasts and platelets or thrombocytes. The size of the images is 360 × 363 pixels, in format jpg, and they were annotated by expert clinical pathologists. The images were captured from individuals without infection, hematologic or oncologic disease and free of any pharmacologic treatment at the moment of blood collection. This high-quality labelled dataset may be used to train and test machine learning and deep learning models to recognize different types of normal peripheral blood cells. To our knowledge, this is the first publicly available set with large numbers of normal peripheral blood cells, so that it is expected to be a canonical dataset for model benchmarking.},
    urldate = {2025-07-17},
    journal = {Data in Brief},
    author = {Acevedo, Andrea and Merino, Anna and Alférez, Santiago and Molina, Ángel and Boldú, Laura and Rodellar, José},
    month = jun,
    year = {2020},
    keywords = {Machine learning, Deep learning, Blood cell automatic recognition, Blood cell images, Blood cell morphology, Hematological diagnosis},
    pages = {105474},
}