A Polyp Segmentation Algorithm Based on Local Enhancement and Attention Mechanism

Accurate polyp segmentation plays a vital role in the early detection and prevention of colorectal cancer. However, the diverse shapes, blurred boundaries, and varying sizes of polyps present significant challenges for automatic segmentation. Existing methods often struggle with effective local feature extraction and modeling long-range dependencies. To overcome these limitations, this paper proposes PolypFormer, which incorporates a local information enhancement module (LIEM) utilizing multi-kernel self-selective attention to better capture texture features, alongside dense channel attention to facilitate more effective feature fusion. Furthermore, a novel cross-shaped windows self-attention mechanism is introduced and integrated into the Transformer architecture to enhance the semantic understanding of polyp regions. Experimental results on five datasets show that the proposed method has good performance in polyp segmentation. On Kvasir-SEG datasets, mDice and mIoU reach 0.920 and 0.886, respectively.