WAveHCT: Wavelet-Attentive Hybrid Convolution-Transformer Network for Breast Cancer Diagnosis in Ultrasound Images

Breast cancer diagnosis using ultrasound imaging remains challenging due to noise, variability in lesion appearance, and several artifacts. To address these concerns, this study proposes WAveHCT, a Wavelet-Attentive Hybrid Convolution-Transformer Network that uses wavelet decomposition and a hybrid architecture to diagnose breast cancers in ultrasound images. The proposed approach begins by applying anisotropic diffusion filtering to the ultrasound images, effectively reducing noise while preserving edge details. A ResNet50-based encoder backbone is then used to extract features from the wavelet-decomposed and anisotropic diffusion-filtered images. These features are integrated using a novel Wavelet-Attentive Feature Fusion (WAFF) module, enabling improved diagnostic performance. Further, the study introduces a hybrid block with convolutional and transformer layers. The transformer layers effectively capture global dependencies, while convolution operations preserve local feature representations. WAveHCT demonstrated superior accuracy, recall, F1-score, and AUC compared to existing methods. Heatmaps generated by WAveHCT exhibited improved localization of clinically relevant features, emphasizing its potential to assist radiologists in diagnosing breast cancers.Clinical relevance-Ultrasound imaging is a cost-effective, non-invasive and non-ionizing method of breast cancer screening. Therefore, developing advanced deeplearning-based tools for diagnosing breast cancer using ultrasound images can enhance radiologists' efficiency and reduce unnecessary invasive biopsies.