A YOLO Ensemble Framework for Detection of Barrett's Esophagus Lesions in Endoscopic Images

Background and Objectives: Barrett's Esophagus (BE) is a precursor to esophageal adenocarcinoma, and early detection is essential to reduce cancer risk. This study aims to develop a YOLO-based ensemble framework to improve the automated detection of BE-associated mucosal lesions on endoscopic images. Methods: A dataset of 3620 annotated endoscopic images was collected from 132 patients. Five YOLO variants, YOLOv5, YOLOv9, YOLOv10, YOLOv11, and YOLOv12, were selected based on their architectural diversity and detection capabilities. Each model was trained individually, and their outputs were integrated using a Non-Maximum Suppression (NMS)-based ensemble strategy. Multiple ensemble configurations were evaluated to assess the impact of fusion depth on detection performance. Results: The ensemble models consistently outperformed individual YOLO variants in recall, the primary evaluation metric. The entire five-model ensemble achieved the highest recall (0.974), significantly reducing missed lesion detections. Statistical analysis using McNemar's test and bootstrap confidence intervals confirmed the superiority in most comparisons. Conclusions: The proposed YOLO ensemble framework demonstrates enhanced sensitivity and robustness in detecting BE lesions. Its integration into clinical workflows can improve early diagnosis and reduce diagnostic workload, offering a promising tool for computer-aided screening in gastroenterology.