Enhanced Medical Image Segmentation via Dual-Window State-Space Encoding and Convolution-Augmented KAN

Medical image segmentation is crucial for precision medicine, yet existing methods struggle to balance global context modeling with fine-grained spatial detail. This study introduces DW-MAKNet, a U-Net-style framework that integrates Dual-Window Mamba (DW-Mamba) and Convolution-Augmented Kolmogorov-Arnold Network (CA-KAN) for robust segmentation. DW-Mamba combines traditional and center-based window scanning to preserve global and local anatomical consistency, while CA-KAN enhances nonlinear representation with convolutional receptive fields. Experiments on five public datasets demonstrate that DW-MAKNet outperforms state-of-the-art models in accuracy and adaptability, achieving up to a 10% improvement in Dice scores on the Abdomen MRI dataset. These results highlight the effectiveness of dual-window state-space encoding and convolution-augmented KAN representation for medical image segmentation. The source code is available at: https://github.com/beginner-cjh/DW-MAKNet.