MedSegKAN: a superior medical image segmentation method based on the improved KAN structure

Medical image segmentation plays an important role in the application of the computer-aided medical therapy field. The traditional approach of merely using Kolmogorov-Arnold Networks instead of the Multilayer Perceptron for medical image segmentation has strong limitations in segmentation performance. In this paper, we propose a superior medical image segmentation method based on the improved Kolmogorov-Arnold Networks structure. Firstly, we use the Gaussian smoothing technique to reduce the noise in the label graph, and the segmentation boundary we gain is smoother than that of the traditional Kolmogorov-Arnold Networks model. Simultaneously, we compute the loss of the grid boundary conditions and add it to the smoothed regularized loss of the grid, helping us to reduce the possible errors in the segmentation process and have better overall performance than the traditional Kolmogorov-Arnold Networks model. Finally, we access the eca_layer at the back end of the Kolmogorov-Arnold Networks to implement an efficient channel attention mechanism, which enables our network to better focus on the important features and enhances the feature representation capability of the model than the traditional Kolmogorov-Arnold Networks model. Experiment shows that our model achieves higher accuracy and better segmentation performance on a variety of medical image segmentation tasks. We obtain 79.73% Dice score and 66.72% IoU score on the BUSI dataset, 92.89% Dice score and 86.73% IoU score on the GlaS dataset, 91.91% Dice score and 85.20% IoU score on the CVC dataset, standing out from many other state-of-the-art models.