Abstract
Health practitioners use hemocytometer to manually counting the blood cells, and it is considered time-consuming, arduous, and expert-dependent. Automated methods are costly, require meticulous maintenance, can lead to misidentify abnormal cells. This research proposed an application that swiftly, precisely, and easily count red and white blood cells. YOLOv5 is used to detect red and white blood cells in digital images. The model is trained on BCCD dataset and BCCD+ALL-IDB1 using YOLOv5s configuration and 736x736 image input size, and achieve 89.9% mAP50 value for red blood cell counting using BCCD dataset. About 17.7% mean absolute percentage error (MAPE) is obtained using YOLO5x configuration with 416x416 image input size tested on BCCD dataset. The YOLOv5s configuration setup with 736x736 image input size gives 10.9% error rate against ALL-IDB1 dataset. The system is developed using Laravel and Flask, and it proficiently detects and counts red and white blood cells.
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