Brain-Inspired Fractional Convolution for Pathological Image Classification

Pathological image diagnosis faces challenges such as complex tissue textures, high heterogeneity, and difficulty in capturing global contextual features. Traditional CNNs, such as ResNet, rely on fixed-size integer-order kernels, which limit their ability to model long-range dependencies and subtle morphological variations. To address these issues, inspired by the brain’s non-local connectivity and memory mechanisms, we propose FracResNet34—an enhanced ResNet34 architecture incorporating Grünwald-Letnikov fractional-order convolutions. This design improves sensitivity to both local details and non-local patterns without increasing parameter complexity. Experiments on PathMNIST, Colorectal Cancer Histology Textures, and LC-L datasets demonstrate that FracResNet34 consistently outperforms conventional ResNet34 and state-of-the-art models such as MedViT and SwinV2 in classification accuracy, robustness, and stability. It achieves lower variance, more negative skewness, and higher kurtosis, indicating more reliable and consistent predictions. These results highlight the promise of fractional-order convolution as a powerful tool for medical image analysis.