TY - JOUR
T1 - Automated blast cell detection for Acute Lymphoblastic Leukemia diagnosis
AU - Khandekar, Rohan
AU - Shastry, Prakhya
AU - Jaishankar, Smruthi
AU - Faust, Oliver
AU - Sampathila, Niranjana
N1 - Funding Information:
We are grateful for the assistance in the Biomedical Research Lab. We would like to thank all the faculty members in the Department of Biomedical Engineering for their kind coordination and help.
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/7
Y1 - 2021/7
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85106319045
UR - https://www.scopus.com/pages/publications/85106319045#tab=citedBy
U2 - 10.1016/j.bspc.2021.102690
DO - 10.1016/j.bspc.2021.102690
M3 - Article
AN - SCOPUS:85106319045
SN - 1746-8094
VL - 68
JO - Biomedical Signal Processing and Control
JF - Biomedical Signal Processing and Control
M1 - 102690
ER -
