Automated blast cell detection for Acute Lymphoblastic Leukemia diagnosis. (July 2021)

• Record Type: Journal Article
• Authors: Khandekar, Rohan; Shastry, Prakhya; Jaishankar, Smruthi; Faust, Oliver; Sampathila, Niranjana
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Acute Lymphoblastic Leukemia (ALL) -- Deep learning -- Blood smear images -- YOLOv4 -- Object detection; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102690
• Languages: English
• ISSNs: 1746-8094

Abstract

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.