DHC-YOLO: Improved YOLOv8 for Lesion Detection in Brain Tumors, Colon Polyps, and Esophageal Cancer

Abstract

The detection of lesions in various diseases remains a challenging task in medical image processing, given the diverse morphologies, sizes, and boundaries of lesions associated with different illnesses. In this paper, we propose an advanced lesion detection model named DHC-YOLO, which integrates Multi-Scale Dilated attention (MSDA) and multi-head self-attention (MHSA) within the YOLOv8 network. The method also introduces an enhanced feature fusion through the Concatenation (Concat) operation in the Feature Pyramid Networks (FPN) structure of YOLOv8. The DHC-YOLO model achieves superior performance in lesion detection by effectively aggregating semantic information across various scales in the attended receptive field, reducing redundancy in self-attention mechanisms without the need for complex operations or additional computational costs. The incorporation of MHSA enhances the network’s ability to extract diverse features, and the Concat operation in FPN improves multi-scale feature fusion. Our evaluations on brain tumor, colonic polyp, and esophageal cancer datasets demonstrate the superiority of our method over baseline YOLOv8 and several state-of-the-art object detection models. Specifically, on the brain tumor dataset, DHC-YOLO achieves mAP50 and mAP50:95 scores of 88.3% and 73.5%, respectively; on the colonic polyp dataset, the scores are 88.8% and 67.2%; and on the esophageal cancer dataset, the scores are 51.3% and 20.7%. These compelling results underscore the robust performance of DHC-YOLO in lesion detection tasks.