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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.