Citation

Escobar, Francesca Isabelle F. and Alipo-on, Jacqueline Rose T. and Novia, Jemima Louise U. and Tan, Myles Joshua Toledo and Abdul Karim, Hezerul and AlDahoul, Nouar (2023) Automated counting of white blood cells in thin blood smear images. Computers and Electrical Engineering, 108. p. 108710. ISSN 0045-7906

Text 4.pdf - Published Version Restricted to Repository staff only Download (5MB)

Abstract

Blood cell counting plays a crucial role in clinical diagnosis to evaluate the overall health condition of an individual. Traditionally, blood cells are manually counted using a hemocytometer; however, this task has been found to be time-consuming and error-prone. Recently, machine learning-based approaches have been employed to effectively automate counting tasks. In this work, the fifth version of the ‘you only look once’ (YOLOv5) object detection method was adopted to automatically detect and count white blood cells (WBCs) in porcine blood smear images. YOLOv5 was chosen because of its speed and accuracy. The dataset used in this study was collected specifically for this WBC counting task. Our experimental results exhibit the high speed and efficiency of YOLOv5 in detecting and counting WBCs, having obtained an accuracy of 89.25% and a mean average precision at 0.5 intersection over union threshold (mAP 0.5) of 99%.

Item Type:	Article
Uncontrolled Keywords:	Counting, Detection, Machine learning, White blood cells, YOLOv5
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	02 May 2023 03:17
Last Modified:	02 May 2023 03:17
URII:	http://shdl.mmu.edu.my/id/eprint/11371

Downloads

Edit (login required)