Experimental comparison of YOLO-based models for white blood cell detection

Abstract

The detection, classification, and analysis of white blood cells, which are one of the fundamental components of the immune system, are of great importance for the diagnosis of diseases such as infections and cancer. Therefore, automated analysis methods that can quickly and accurately identify and classify white blood cells in peripheral blood smear images are highly significant. In this study, four different YOLO-based models were used for the detection and classification of white blood cells, and their performances were comparatively evaluated. For the experimental studies, the LeukemiaAttri dataset, designed for leukemia diagnosis, was utilized. The results demonstrated that the YOLOv9t and YOLOv11n models outperformed the other models. Additionally, the class-based performance of the YOLOv9t model was examined. These findings indicate that YOLO-based methods are effective for the detection and classification of white blood cells.