Benchmarking YOLO Variants for Enhanced Blood Cell Detection

Blood cell detection provides a significant amount of information about a person's health, aiding in the diagnosis and monitoring of various medical conditions. Red blood cells (RBCs) carry oxygen, white blood cells (WBCs) play a role in immune defence, and platelets contribute to blood clotting. Changes in the composition of these cells can signal various physiological and pathological conditions, which makes accurate blood cell detection essential for effective medical diagnosis. In this study, we apply convolutional neural networks (CNNs), a subset of deep learning (DL) techniques, to automate blood cell detection. Specifically, we compare the performance of multiple variants of the You Only Look Once (YOLO) model, including YOLO v5, YOLO v7, YOLO v8 (in medium, small and nano configurations), YOLO v9c and YOLO v10 (in medium, small and nano configurations), for the task of detecting RBCs, WBCs and platelets. The results show that YOLO v5 achieved the highest mean average precision (mAP50) of 93.5%, with YOLO v10 variants also performing competitively. YOLO v10m achieved the highest precision for RBC detection at 85.1%, while YOLO v10n achieved 98.6% precision for WBC detection. YOLO v5 demonstrated the highest precision for platelets at 88.8%. Overall, YOLO models provided high accuracy and precision in detecting blood cells, making them suitable for medical image analysis. In conclusion, the study demonstrates that the YOLO model family, especially YOLO v5, holds significant potential for advancing automated blood cell detection. These findings can help improve diagnostic accuracy and contribute to more efficient clinical workflows.