VesselMetaKAN: vessel-guided meta-learned interpretable classification for diabetic retinopathy grading.

Introduction: Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide, affecting approximately 93 million people globally. Accurate automated grading is critical for large-scale public health screening, yet existing deep learning methods face challenges related to structure-coupled sparse evidence and domain shift across imaging conditions. Methods: We propose VesselMetaKAN, a two-stage framework integrating explicit vessel guidance with meta-learning. Stage 1 employs GMF-SwinUnet for topology-aware vessel segmentation using Frangi-guided attention fusion, reliability gating, and clDice loss. Stage 2 employs KAN-MAML, combining Kolmogorov-Arnold networks with radial basis function expansions as an interpretable decision head and Reptile-style meta-learning over augmentation-defined tasks. Vessel probability maps from Stage 1 guide Stage 2 classification through vessel-gated feature fusion. Results: On APTOS 2019, VesselMetaKAN achieved 74.9 ± 0.4% accuracy, 58.7 ± 0.6% macro-F1, and 0.838 ± 0.005 QWK over five runs. The best single run reached 75.44% accuracy, 59.44% macro-F1, and 0.843 QWK, outperforming EfficientNet-B4 on the principal grading metrics with paired bootstrap significance ( Discussion: VesselMetaKAN provides a principled, structure-aware, and interpretable solution for robust DR grading in public health screening programs.