Polyp Segmentation Algorithm Based on the Dual Attention and Fusion Mechanism

Polyp segmentation plays a critical role in enhancing the accuracy of colorectal cancer screening and reducing polyp miss rates. The segmentation accuracy of existing algorithms is significantly limited due to challenges such as polyp morphological diversity, complex mucosal attachments, and boundary ambiguity. To address the limitations of insufficient feature extraction, information redundancy, and imbalance between global and local information fusion, a Dual Attention and Fusion Mechanism Network (DAFM-Net) is proposed, which achieves complementary feature fusion through multi-module collaborative optimization. Firstly, the Multi-scale Convolutional Patch Aware module (MCPA) employs multi-branch convolution and local attention mechanisms to extract multi-granular features, improving the characterization of irregular polyps. Secondly, the Cross-layer Aware Selective Fusion module (CASF) adaptively weights deep and shallow features to reduce redundant information and enhance semantic complementarity. Finally, the Dual Context Enhanced Attention module (DCEA) integrates global and local attention mechanisms to synergistically optimize global structure perception and local boundary details. Experimental results demonstrate the effectiveness of the algorithm, which outperforms state-of-the-art models on five publicly available polyp datasets. The proposed network exhibits superior segmentation accuracy and robustness, particularly in complex backgrounds, irregular morphologies, and multi-scale polyp scenarios.