SAFH-Net: A Hybrid Network With Shuffle Attention and Adaptive Feature Fusion for Enhanced Retinal Vessel Segmentation

ABSTRCT Segmenting retinal blood vessels is critical for the early detection of retinal abnormalities. While significant progress has been achieved in vessel segmentation through deep learning techniques, existing methodologies still struggle with the effectiveness of extracting and integrating local-global features. To overcome these challenges, this paper introduces SAFH-Net, a hybrid end-to-end network architecture that synergistically integrates Swin Transformer and CNN with innovative shuffle attention mechanisms and adaptive feature fusion. Specifically, a parallel encoder architecture employs a Convolutional (Conv) block with a residual structure for local feature extraction alongside a hierarchical Swin Transformer with Shifted-Window Multi-head Self-Attention (SW-MSA) for global context modeling, thereby achieving comprehensive feature capture with minimal additional parameter overhead. Then, an improved Spatial Attention Feature Fusion (SAFF) module is used to enable pixel-level adaptive weighting for optimal local-global feature integration. Additionally, cross-channel and cross-spatial shuffle operations enhance the interaction between local details and global information efficiency while suppressing redundant information. We achieved accuracies of 97.28%, 97.55%, and 97.60% on the DRIVE, STARE, and CHASE_DB1 datasets, respectively. A series of experimental results demonstrates that the proposed model significantly outperforms other advanced methods in segmentation performance.