Lymphocyte Detection Method Based on Improved YOLOv5

To address the limitations of traditional burdensome and time-consuming manual diagnosis of Sjogren's syndrome, this study proposes and implements an improved version of YOLOv5s algorithm, named YOLOv5s-MSS. Using YOLOv5s-MSS, we are able to detect lymphocytic infiltrative lesions in pathological images and provide assistance for pathological diagnosis. Given the small size of lymphocytes and the difficulty in distinguishing them, we made four improvements to the YOLOv5s model. Firstly, we replace the original CIOU loss function with the Focal-SIOU loss function to accelerate model convergence and improve the detection accuracy. Additionally, we introduce the multi-head self-attention module into the backbone to enhance the model's ability to capture long range dependencies and overcome the challenges posed by complex background. Furthermore, we introduce the Shuffle Attention module into the neck, which enhances the model's ability to fuse features from both spatial and channel dimensions. Finally, we remove the 1/32 downsampling section in the neck and the corresponding large object detection head. This not only enhances accuracy but also reduces parameters and model complexity. Experimental results show that YOLOv5s-MSS achieves a mAP, Precision, and Recall of 93.2%, 87.2%, and 89%, representing increases of 2.9%, 2.6%, and 2.8% compared to the original YOLOv5s model. Additionally, YOLOv5s-MSS reduces the parameters by 28.2%. These results demonstrate the effectiveness and value of YOLOv5s-MSS for lymphocyte detection.