RT Journal Article
SR Electronic
T1 Real-time Detection of Acute Lymphoblastic Leukemia Cells Using Deep Learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.10.22.513362
DO 10.1101/2022.10.22.513362
A1 Chen, Emma
A1 Shalaginov, Mikhail Y.
A1 Liao, Rory
A1 Zeng, Tingying Helen
YR 2022
UL http://biorxiv.org/content/early/2022/10/24/2022.10.22.513362.abstract
AB Acute lymphoblastic leukemia (ALL) is one of the most common types of cancer among children. It can rapidly become fatal within weeks, hence early diagnosis is critical. Problematically, the ALL diagnosis mainly involves manual blood smear analysis relying on the expertise of medical professionals, which is error-prone and time-consuming. Thus, it is important to develop artificial intelligence tools that will identify leukemic cells from a microscopic image faster, more accurately, and cheaper. Here, we investigate the capabilities of a traditional convolutional neural network (CNN) and You Only Look Once (YOLO) models for real-time detection of leukemic cells. The YOLOv5s model shows 97.2% accuracy for the task of object detection of ALL cells, with the inference speed allowing 80 image frames to be processed per second. These new findings can provide valuable insight in applying real-time object detection algorithms for improving the efficiency of blood cancer diagnosis.Competing Interest StatementThe authors have declared no competing interest.