Research on Blood Cell Detection and Counting Based on YOLO-BC Algorithm

Abstract

Abstract Blood cell detection and counting have always been of great medical importance because healthcare professionals can identify possible abnormalities in patients based on the numbers of different types of blood cells. However, traditional counting methods have certain limitations, such as smaller blood cells being ignored or misclassified, and the accuracy of identifying scenes with occlusion and overlap between blood cells is low. To solve the problem of blood cells being missed or misidentified in existing models, this study introduces the improved YOLO-BC algorithm to solve the pixel-level differences of different categories of blood cells by combining efficient multi-scale attention and full-dimensional dynamic convolution models, thereby achieving fast and accurate identification and counting of blood cells. The BCCD (Blood Cell Count and Detection) dataset was used for related experiments and performed data augmentation. The mAP@50 score based on YOLO-BC is 3.1% higher than that of YOLOv8, the value of mAP@50:95 increases by 3.7%, and F1-score increases by 2% on the same dataset and iou parameters, where small objects such as platelets can also be clearly detected. YOLO-BC shows a certain degree of applicability for automated testing of blood cells by experimental results.