# Capsule-ConvKAN: A Hybrid Neural Approach to Medical Image Classification Source: [Capsule-ConvKAN: A Hybrid Neural Approach to Medical Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:59:41.376Z Authors: Laura Pitukov\\'a, Peter Sin\\v{c}\\'ak, L\\'aszl\\'o J\\'ozsef Kov\\'acs, Peng Wang Published: 2025-07-08T21:51:05.000Z ## Abstract This study conducts a comprehensive comparison of four neural network architectures: Convolutional Neural Network, Capsule Network, Convolutional Kolmogorov-Arnold Network, and the newly proposed Capsule-Convolutional Kolmogorov-Arnold Network. The proposed Capsule-ConvKAN architecture combines the dynamic routing and spatial hierarchy capabilities of Capsule Network with the flexible and interpretable function approximation of Convolutional Kolmogorov-Arnold Networks. This novel hybrid model was developed to improve feature representation and classification accuracy, particularly in challenging real-world biomedical image data. The architectures were evaluated on a histopathological image dataset, where Capsule-ConvKAN achieved the highest classification performance with an accuracy of 91.21%. The results demonstrate the potential of the newly introduced Capsule-ConvKAN in capturing spatial patterns, managing complex features, and addressing the limitations of traditional convolutional models in medical image classification. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()