Breast cancer histopathology image classification using transformer with discrete wavelet transform

Abstract

Early diagnosis of breast cancer using pathological images is essential to effective treatment. With the development of deep learning techniques, breast cancer histopathology image classification methods based on neural networks develop rapidly. However, these methods usually capture features in the spatial domain, rarely consider frequency feature distributions, which limits classification performance to some extent. This paper proposes a novel breast cancer histopathology image classification network, called DWNAT-Net, which introduces Discrete Wavelet Transform (DWT) to Neighborhood Attention Transformer (NAT). DWT decomposes inputs into different frequency bands through iterative filtering and downsampling, and it can extract frequency information while retaining spatial information. NAT utilizes Neighborhood Attention (NA) to confine the attention computation to a local neighborhood around each token to enable efficient modeling of local dependencies. The proposed method was evaluated on the BreakHis and Bach datasets, yielding impressive image-level recognition accuracy rates. We achieve a recognition accuracy rate of 99.66% on the BreakHis dataset and 91.25% on the BACH dataset, demonstrating competitive performance compared to state-of-the-art methods.