GSCCANet: Dual Decoder Network Fusing Edge Focus and Global Channel Attention for Precise Segmentation of Colonic Polyps

Colorectal cancer is the second most common cancer globally. Its high mortality necessitates early polyp detection to mitigate the risk of the disease. However, conventional segmentation methods are susceptible to noise interference and have a limited accuracy in complex environments. To address these challenges, we propose GSCCANet with an encoder-dual decoder co-design. The encoder employs hybrid Transformer (MiT) for efficient multi-scale global feature extraction. Dual decoders collaborate via SAFM and REF-RA modules to enhance segmentation precision through global semantics and boundary refinement. In particular, SAFM enhances lesion coherence via channel-space attention fusion, while REF-RA strengthens low-contrast edge response using high-frequency gradients and reverse attention, optimized through progressive fusion. Additionally, combined Focal Loss and Weighted IoU Loss mitigate the problem of undetected small polyps. Experiments on five datasets show GSCCANet surpasses baselines. It achieves 94.7% mDice and 90.1% mIoU on CVC-ClinicDB (regular) and 80.1% mDice and 72.5% mIoU on ETIS-LaribPolypDB (challenging). Cross-domain tests (CVC-ClinicDB $\to$ Kvasir) confirm strong adaptability with 0.2% mDice fluctuation. These results prove that GSCCANet offers high-precision and generalizable solutions through global–local synergy, edge enhancement, and efficient computation.