Deep-learning-based breast cancer image diagnosis is currently a prominent and growingly popular area of research. Existing convolutional-neural-network-related methods mainly capture breast cancer image features based on spatial domain characteristics for classification. However, according to digital signal processing theory, texture images usually contain repeated patterns and structures, which appear as intense energy at specific frequencies in the frequency domain. Motivated by this, we make an attempt to explore a breast cancer histopathology classification application in the frequency domain and further propose a novel multi-branch spectral channel attention network, i.e., the MbsCANet. It expands the interaction of frequency domain attention mechanisms from a multi-branch perspective via combining the lowest frequency features with selected high frequency information from two-dimensional discrete cosine transform, thus preventing the loss of phase information and gaining richer context information for classification. We thoroughly evaluate and analyze the MbsCANet on the publicly accessible BreakHis breast cancer histopathology dataset. It respectively achieves the optimal image-level and patient-level classification results of 99.01% and 98.87%, averagely outperforming the spatial-domain-dominated models by a large margin, and visualization results also demonstrate the effectiveness of the MbsCANet for this medical image application.

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.