A Scaled Denoising Attention-Based Transformer for Breast Cancer Detection and Classification

Breast cancer significantly threatens women’s health, and early, accurate diagnosis via mammogram screening has considerably reduced overall disease burden and mortality. Computer-Aided Diagnosis (CAD) systems have been used to assist radiologists by automatically detecting, segmenting, and classifying medical images. However, precise breast lesion diagnosis has remained challenging. In this paper, we propose a novel approach for breast cancer detection and classification in screening mammograms. Our model is a hybrid of CNN and Transformers, specifically designed to detect and classify breast cancer. The model first utilizes a depthwise convolution-based hierarchical backbone for deep feature extraction, coupled with an Enhancement Feature Block (EFB) to capture and aggregate multi-level features to the same scale. Subsequently, it introduces a transformer with Scale-Denoising Attention (SDA) to simultaneously capture global features. Finally, the model employs regression and classification heads for detecting and localizing lesions and classifying mammogram images. We evaluate the proposed model using the CBIS-DDSM dataset and compare its performance with those of state-of-the-art models. Our experimental results and extensive ablation studies demonstrate that our method outperforms others in both detection and classification tasks.