Stacking Ensemble Learning for Accurate Polyp Segmentation

Polyp segmentation in colonoscopy images is a crucial task for early detection and prevention of colorectal cancer. In this study, we propose an ensemble learning approach combining ResNet50-U-Net and ResNet50-Polyp, two fine-tuned deep learning models designed for gastrointestinal disease segmentation. The models were trained on the Kvasir-SEG dataset, and ensemble learning techniques were applied to enhance segmentation accuracy. Our approach demonstrates state-of-the-art performance, achieving an accuracy of 0.9689, precision of 0.9200, recall of 0.8800, F1-Score of 0.9065, and IoU of 0.7544. Comparative analysis with existing methods confirms the robustness and efficiency of our model in accurately identifying Polyp. The proposed ensemble model offers a reliable solution for Polyp segmentation, contributing to improved diagnostic in clinical settings.