H-fusion SEG: dual-branch hyper-attention fusion network with SAM integration for robust skin disease segmentation

Accurate dermoscopic lesion segmentation is challenging because existing methods struggle to preserve fine-grained local structures while capturing long-range semantic context, leading to reduced robustness against unclear boundaries, imaging artifacts, and dataset shifts. We propose Hyper-Fusion Segmentation (H-Fusion SEG), a dual-branch framework that combines a boundary-sensitive U-Net encoder-decoder with a Segment Anything Model branch to jointly extract high-resolution local details and robust global semantics. A novel hyper-attention fusion module adaptively integrates these heterogeneous features and is optimized with boundary-aware objectives to enhance delineation and interpretability. On the ISIC-2016 dataset, H-Fusion SEG achieves IoU = 0.8775 and Dice = 0.9269 (+ 1.28% IoU, + 1.38% Dice over baselines), and on ISIC-2018, it achieves IoU = 0.9329 and Dice = 0.9629 (+ 8.69% IoU, + 6.69% Dice over baselines), with strong generalization to the HAM10000 dataset. These gains are particularly pronounced for complex lesions with indistinct or ambiguous boundaries. The proposed framework offers a flexible and generalizable solution for medical image segmentation, with promising potential for precise and reliable computer-aided diagnostic tools in dermatology. Code is available at: https://github.com/AnasHXH/Skin-DiseaseS-Segmentation .