Abstract
The integration of deep learning in medical image analysis has driven significant progress, especially in the domain of automatic blood cell detection. While the YOLO series of algorithms have become widely adopted as a real-time object detection approach, there is a need for further refinement for the detection of small targets like blood cells and in low-resolution images. In this context, we introduce SSW-YOLO, a novel algorithm designed to tackle these challenges. The primary innovations of SSW-YOLO include the use of a spatial-to-depth convolution (SPD-Conv) layer to enhance feature extraction, the adoption of a Swin Transformer for multi-scale attention mechanisms, the simplification of the c2f module to reduce model complexity, and the utilization of Wasserstein distance loss (WDLoss) function to improve localization accuracy. With these enhancements, SSW-YOLO significantly improves the accuracy and efficiency of blood cell detection, reduces human error, and consequently accelerates the diagnosis of blood disorders while enhancing the precision of clinical diagnoses. Empirical analysis on the BCCD blood cell dataset indicates that SSW-YOLO achieves a mean average precision (mAP) of 94.0%, demonstrating superior performance compared to existing methods.