MSFKAN: A Multi-Scale Feature Prediction Network Combined with KAN for Medical Image Classification

作者：Jianyun Gao, Yuan Liao, Zerui Zhang, Shu Li, Hao Wang · 发表于：Neural Processing Letters · 年份：2025 · DOI：10.1007/s11063-025-11782-6 · 被引用次数：7 · 研究领域：AI in cancer detection、Brain Tumor Detection and Classification、Medical Imaging and Analysis

The characteristics of the lesion areas in medical images are complex, and existing fully connected layer-based neural networks still cannot address the issue of linear kernels, making them inadequate for handling the nonlinear classification problem of medical image data. This paper investigates a multi-scale feature joint prediction Kolmogorov-Arnold Network convolutional network combined with a spatial attention mechanism for medical image classification applications. In this article, we propose the Multi-Scale Feature prediction network combined with Kolmogorov-Arnold Network. In this model, convolutional blocks are concatenated to output feature maps of different scales. These feature maps are then passed through a spatial attention module, and the output is weighted and summed using learnable weights in the Kolmogorov-Arnold Network layer for classification. The model is trained, tuned, and tested on three publicly available medical image datasets, including the skin cancer image dataset, COVID-19 lung CT dataset, and brain tumor MRI dataset. Experimental results show that the proposed model demonstrates strong performance in classification tasks across four datasets, achieving an accuracy of 87.27% for binary skin cancer classification, 94.12% for the three-class COVID-19 CT classification, and 97.48% for the four-class brain tumor CT classification. This performance outperforms the classification accuracy of the selected comparison methods. Additionally, we applied a ...