Abstract:
The counting of blood cells is an important test that aids in the diagnosis of certain disorders. The
technique of manually counting abnormal blood cells by an experienced specialist is exceedingly
arduous, time-consuming, and imprecise, with a substantial risk of error. Automated detection of
blood cells using image processing techniques is gaining prominence as a result of recent
advancements. In this research, the authors offer a deep learning strategy for automatically
identifying and counting three types of blood cells using the ‘you only look once' (YOLO) object
detection and classification algorithm in this paper. The YOLO framework has been taught to
automatically recognize and count red blood cells, white blood cells, and platelets using a modified
configuration BCCD Dataset of blood smear image. Overall, our automated blood cells counting
system is fast and more efficient to detect blood cells.
