MA-KANet: Enhancing retinal vessel segmentation through multi-scale feature fusion and attention mechanisms

We propose Multi-Scale Attention Kolmogorov–Arnold Network (MA-KANet), a novel retinal vessel segmentation framework addressing fine-scale structures and low contrast challenges. Our method integrates Multi-scale Dynamic Fusion (MDF) with 3D convolutional interactions to prevent information loss during downsampling, and Scale-Context Attention Fusion (SCAF) for dynamic feature recalibration in cluttered regions. Kolmogorov–Arnold Network units in decoder stages capture nonlinear dependencies beyond traditional convolutions. MA-KANet achieves state-of-the-art results: 98.80% AUC on DRIVE, 99.05% AUC on CHASEDB1, and 99.17% AUC on FIVES dataset, demonstrating superior performance and generalization across diverse vessel patterns, establishing new benchmarks for retinal vessel segmentation.