Improvement of colon polyp segmentation method based on TransFuse model

To address issues such as fuzzy boundaries, lack of small polyp detection, and image fragmentation in colon polyp segmentation, an enhanced TransFuse model is proposed. By refining the BiFusion and up modules and effectively combining deep and shallow feature information, the improved TransFuse model achieves more accurate colon polyp segmentation. Experimental results on the Kvasir-SEG dataset demonstrate enhancements in accuracy, with increases of 0.7%, 2.3%, and 1.1% in Acc, IOU, and Dice metrics, respectively. Compared to the original model, this approach detects and segments colon polyps more accurately.