Abstract
In the field of biomedical science, blood cell detection in microscopic images is crucial for aiding physicians in diagnosing blood-related diseases and plays a pivotal role in advancing medicine toward more precise and efficient treatment directions. Addressing the time-consuming and error-prone issues of traditional manual detection methods, as well as the challenge existing blood cell detection technologies face in meeting both high accuracy and real-time requirements, this study proposes a lightweight blood cell detection model based on YOLOv8n, named GPMB-YOLO. This model utilizes advanced lightweight strategies and PGhostC2f design, effectively reducing model complexity and enhancing detection speed. The integration of the simple parameter-free attention mechanism (SimAM) significantly enhances the model's feature extraction ability. Furthermore, we have designed a multidimensional attention-enhanced bidirectional feature pyramid network structure, MCA-BiFPN, optimizing the effect of multi-scale feature fusion. And use genetic algorithms for hyperparameter optimization, further improving detection accuracy. Experimental results validate the effectiveness of the GPMB-YOLO model, which realized a 3.2% increase in mean Average Precision (mAP) compared to the baseline YOLOv8n model and a marked reduction in model complexity. Furthermore, we have developed a blood cell detection system and deployed the model for application. This study serves as a valuable reference for the efficient detection of blood cells in medical images.