A Comparative Analysis of YOLOv5nu, YOLOv8n, and YOLOv11n Models for Blood Cell Detection and Classification

Object detection, which detects and classifies objects, is widely used in different fields. One such field is in the medical field, and more specifically it can be used to detect molecules in microscopy images to improve blood test efficiency. Nowadays, plentiful hospital labs are still manually counting blood cells which is time-consuming, and it is likely to make manual errors. A solution to this problem is to enable accurate object detection specifically designed for identifying molecules. To achieve this goal, the comparison analysis of different YOLO models, one of the most popular models in object detection, on a blood cell dataset is valuable as it evaluates the models accuracy, which provides insight into their strengths and weaknesses. The paper considers three types of YOLO models, YOLOv5nu, YOLOv8n, and YOLO11n models, on detecting and classifying red blood cells (RBC), white blood cells (WBCs), and Platelets. The experiment results show that all three models have high precision and recall rates, which means that they can identify most of the molecules accurately. This indicates a promising future of integrating object detection on blood cell count to speed up medical analysis.