Automated counting of white blood cells in thin blood smear images

被引：11

作者：

Escobar, Francesca Isabelle F. ^{[1
]}

Alipo-on, Jacqueline Rose T. ^{[1
]}

Novia, Jemima Louise U. ^{[1
]}

Tan, Myles Joshua T. ^{[1
,2
]}

Karim, Hezerul Abdul ^{[3
]}

AlDahoul, Nouar ^{[3
,4
]}

机构：

[1] Univ St La Salle, Dept Nat Sci, La Salle Ave, Bacolod 6100, Philippines

[2] Univ St La Salle, Dept Chem Engn, La Salle Ave, Bacolod 6100, Philippines

[3] Multimedia Univ, Fac Engn, Cyberjaya 63100, Selangor, Malaysia

[4] NYU, Comp Sci, Abu Dhabi, U Arab Emirates

来源：

COMPUTERS & ELECTRICAL ENGINEERING | 2023年 / 108卷

关键词：

Counting; Detection; Machine learning; White blood cells; YOLOv; 5;

D O I：

10.1016/j.compeleceng.2023.108710

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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%.

引用

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