Meta-learning and Residual Block Enhanced YOLO for Accurate Detection of Gastrointestinal Pathology Lesions

Early identification and accurate diagnosis of gastrointestinal diseases, particularly gastric cancer, are paramount for enhancing patient survival rates and treatment outcomes. However, diagnosing these diseases can be challenging, especially when symptoms are mild or absent. Endoscopy, a standard diagnostic tool, relies heavily on the endoscopist's expertise. Integrating artificial intelligence AD with endoscopic imaging has the potential to assist in diagnosis, reduce missed cases, and expedite timely treatment. Previous studies have focused on refining disease classification and and improving diagnostic accuracy, often neglecting issues of data reliability and imbalance. This study proposes a novel approach utilizing model-agnostic meta-learning MAML strategies to address the challenges posed by sparse and imbalanced medical image data. We introduce the YOLO-MR model, which incorporates meta-recognition mechanisms and residual blocks into the YOLO framework. Experimental results demonstrate that the traditional YOLO model achieves an average precision mAP of only 41.7 on imbalanced data, highlighting the negative impact of data imbalance. Traditional data augmentation techniques improve the mAP to 65.2 . whereas our proposed YOLO-MR model achieves an impressive mAP of 96 , representing a significant improvement of 54.3 over the traditional model. This enhancement effectively reduces the diagnostic accuracy gap between different disease categories and mitigates the issue of data imbalance. Furthermore, our research validates the strong potential of advanced techniques such as MAML and residual blocks in resource-limited medical image recognition tasks. These findings provide valuable insights into addressing the challenges of limited and imbalanced medical data in the healthcare field.