FKAN-GMFNet: Fourier Kolmogorov-Arnold-based Group Multi-scale Fusion Network for Aneurysm Image Segmentation

Abstract

KAN-based networks, while offering improved interpretability compared to traditional models used in medical image segmentation, often struggle with limited adaptability to diverse imaging environments, making them less ideal for such tasks. To address this issue, we propose a Fourier Kolmogorov–Arnold–based (FKAN) Group Multi–scale Fusion Network, termed FKAN–GMFNet, which incorporates an FKAN layer into a labeled intermediate representation, introducing an Enhanced–FKAN block. Furthermore, we develop the Attention Group Multi–scale Aggregation (ATGMA) module, which leverages attention mechanisms and grouping strategies to effectively fuse feature masks with both high– and low–scale feature information, thereby achieving a comprehensive multi-scale feature representation. Extensive experiments demonstrate that the FKAN GMFNet significantly outperforms seven state–of–the–art methods in both Dice and IoU scores, where the Dice and IoU scores for the IAS–L dataset are 88.82% and 80.09%, respectively. Code is available at https://github.com/zx123868/FKAN-GMFNet.