PT  - JOURNAL ARTICLE
AU  - Chen, Emma
AU  - Shalaginov, Mikhail Y.
AU  - Liao, Rory
AU  - Zeng, Tingying Helen
TI  - Real-time Detection of Acute Lymphoblastic Leukemia Cells Using Deep Learning
AID  - 10.1101/2022.10.22.513362
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.10.22.513362
4099  - http://biorxiv.org/content/early/2022/10/24/2022.10.22.513362.short
4100  - http://biorxiv.org/content/early/2022/10/24/2022.10.22.513362.full
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.