Breast Cancer Histopathological Image Classification Based on High-Order Modeling and Multi-Branch Receptive Fields

Existing convolutional neural network (CNN) methods primarily depend on first-order feature modeling, which makes it challenging to effectively capture higher-order features in breast cancer histopathological images. Additionally, due to the limitations of the receptive field, CNNs have difficulty capturing long-range dependencies, thereby limiting the integration of global information. To address this, inspired by the strengths of high-order statistical features and extended receptive fields in visual tasks, this study proposes a novel high-order receptive field network (HoRFNet). Specifically, HoRFNet expands the receptive field and improves the model’s contextual awareness of pathological tissue structures by introducing a multi-branch convolutional structure with convolution kernels of varying sizes, along with dilated convolution layers. Additionally, HoRFNet integrates a matrix power normalization strategy in the covariance pooling module to model the global interactions between convolutional features, thereby improving the higher-order representation of complex textures and structural relationships in tissue images. The BreakHis dataset shows that HoRFNet achieves an image level classification accuracy of 99.50% and a patient level classification accuracy of 99.23%, significantly outperforming existing methods and demonstrating its effectiveness.