DCAU-KAN: Dual Cross Attention Network for Segmenting Medical Images

Currently, in the field of medical image segmentation, deep learning methods have shown significant advantages and have been widely applied. However, mainstream U-shaped segmentation models still face two critical and unresolved challenges in practical applications: on the one hand, traditional U-Net and its improved variants rely on multi-layer perceptron (MLP) for feature transformation, leading to severe parameter redundancy and heavy computational overhead; on the other hand, these models lack targeted guidance from image inherent features in the contextual information acquisition stage, which results in insufficient mining of position and channel correlation features. To address these, we introduce DCAU-KAN, a improved U-shaped architecture that fuses Kolmogorov–Arnold Networks (KANs) with a novel dual cross attention block (DCA-Block). The DCA block guides the acquisition of contextual information by introducing the inherent features of the image (position and channel features), thereby enhancing the feature extraction capability. Furthermore, we use the residual skip multi-path (RSMP block) to integrate the features of the encoder and decoder, increasing the representation of the features and reducing feature confusion. To evaluate our method, we conducted experiments using five widely recognized medical datasets. The experimental results demonstrate that the improved model achieves an average IoU of 84.02% and a DSC of 90.59% across five datasets, representing an improvement of 3.65% and 2.97%, respectively, compared to the original model.