About Dataset

As a highly prevalent cancer, the definitive diagnosis of acute lymphoblastic leukemia (ALL) requires invasive, expensive, and time-consuming diagnostic tests. ALL diagnosis using peripheral blood smear (PBS) images plays a vital role in the initial cancer screening from non-cancer cases. The examination of these PBS images by laboratory users is riddled with problems such as diagnostic error because the non-specific nature of ALL signs and symptoms often leads to misdiagnosis.

The images of this dataset were prepared in the bone marrow laboratory of Taleqani Hospital (Tehran, Iran). This dataset consisted of 3256 PBS images from 89 suspected of ALL patients whose blood samples were prepared and stained by skillful laboratory staff. This dataset is divided into two classes benign and malignant. The former comprises hematogones; the latter is the ALL group with three subtypes of malignant lymphoblasts: Early Pre-B, Pre-B, and Pro-B ALL. All the images were taken using a Zeiss camera in a microscope with 100x magnification and saved as JPG files. A specialist using the flow cytometry tool made the definitive determination of the types and subtypes of these cells. After color thresholding-based segmentation in the HSV color space, we also provide segmented images.

Paper: A Fast and Efficient CNN Model for B-ALL Diagnosis and its Subtypes Classification using Peripheral Blood Smear Images
Source code: https://github.com/MehradAria/ALL-Subtype-Classification

If you use this dataset in your research, please credit the authors.
Data Citation:
Mehrad Aria, Mustafa Ghaderzadeh, Davood Bashash, Hassan Abolghasemi, Farkhondeh Asadi, and Azamossadat Hosseini, “Acute Lymphoblastic Leukemia (ALL) image dataset.” Kaggle, (2021). DOI: 10.34740/KAGGLE/DSV/2175623.

Publication Citation:
Ghaderzadeh, M, Aria, M, Hosseini, A, Asadi, F, Bashash, D, Abolghasemi, H. A fast and efficient CNN model for B-ALL diagnosis and its subtypes classification using peripheral blood smear images. Int J Intell Syst. 2022; 37: 5113- 5133. doi:10.1002/int.22753