Dual‐branch feature extraction network combined with Transformer and CNN for polyp segmentation

Abstract To overcome the difficulty of accurate polyp segmentation, a novel encoder–decoder network DFETC‐Net is proposed, in which two encoders based on Swin Transformer and CNN are utilized to extract the global and local features respectively. Further, a new self‐attention and convolution feature fusion module is designed to fuse the two branch features to enhance the feature representative capability and alleviate the influence of the semantic gap. In the bottleneck, a new multi‐feature pyramid pooling module fuses all deep features from two branches to obtain multi‐scale information and promote segmentation accuracy. The coordinate attention is used in the skip connections between two shallow CNN blocks and corresponding decoder blocks to pay more attention to doubtful and complicated regions. Extensive experiments demonstrate the proposed network outperforms several state‐of‐the‐art methods in terms of both qualitative effects and quantitative measurements. All codes are available on https://github.com/LYJieH/DFETC-NET .