Automated blast cell detection for Acute Lymphoblastic Leukemia diagnosis

KHANDEKAR, Rohan, SHASTRY, Prakhya, JAISHANKAR, Smruthi, FAUST, Oliver and SAMPATHILA, Niranjana (2021). Automated blast cell detection for Acute Lymphoblastic Leukemia diagnosis. Biomedical Signal Processing and Control, 68, p. 102690.

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Official URL: https://www.sciencedirect.com/science/article/pii/...
Link to published version:: https://doi.org/10.1016/j.bspc.2021.102690
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    Abstract

    Acute Lymphoblastic Leukemia (ALL) is a cancer of the blood cells which is characterized by a large number of immature lymphocytes, known as blast cells (myeloblasts). To aid the ALL diagnosis, we propose to automate the blast cell detection using Artificial Intelligence (AI). Our automation system incorporates an object detection method that predicts leukemic cells from microscopic blood smear images. We have implemented version 4 of the You Only Look Once (YOLOv4) algorithm for both cell detection and cell classification. As such, the classification was set up as a binary problem, where each cell was labeled as either blast cells (ALL) or healthy cells (HEM). The Object Detection algorithm was trained and tested with images from the ALL_IDB1 and C_NMC_2019 dataset. The mAP (Mean Average Precision) was 96.06 % for the ALL-IDB1 dataset and 98.7 % for the C_NMC_2019 dataset. Both models were trained with Google Colaboratory using a Nvidia Tesla P-100 GPU. This proposed blast cell detection algorithm might be used as an adjunct tool during pre-screening where it can help to detect Leukemia based on microscopic blood smear images.

    Item Type: Article
    Additional Information: ** Article version: AM ** Embargo end date: 19-05-2022 ** From Elsevier via Jisc Publications Router ** Licence for AM version of this article starting on 19-05-2022: http://creativecommons.org/licenses/by-nc-nd/4.0/ **Journal IDs: issn 17468094 **History: issue date 31-07-2021; published_online 19-05-2021; accepted 24-04-2021
    Identification Number: https://doi.org/10.1016/j.bspc.2021.102690
    Page Range: p. 102690
    SWORD Depositor: Colin Knott
    Depositing User: Colin Knott
    Date Deposited: 26 May 2021 12:13
    Last Modified: 15 Aug 2021 10:26
    URI: http://shura.shu.ac.uk/id/eprint/28683

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