Breast cancer detection in mammography images using Neighborhood Attention transformer and Shearlet Transform

Breast cancer is a leading cause of cancer-related deaths among women. Advances in early diagnosis and treatment, particularly through screening mammography, have reduced mortality rates by enabling the detection of small tumors. Recently, artificial intelligence (AI) and advanced computer vision models have further improved breast cancer detection and diagnosis. In this research, we have developed a novel model for detecting breast cancer in mammography images by extracting rich and suitable features. Our model utilizes the Neighborhood Attention Transformer, which enhances local feature processing by focusing on neighborhood attention alongside global and long-range features. This is crucial for analyzing masses within and at the boundaries. Additionally, we incorporate the Shearlet Transform to enhance feature extraction by capturing frequency-domain features, essential for precise edge and texture analysis in mammographic images. The Shearlet Transform's ability to manage anisotropic features and its strong localization in both spatial and frequency domains make it particularly effective. Denoising is another key aspect, as mammograms often contain noise from imaging conditions and devices. To address this, our model applies Shearlet-based adaptive shrinkage denoising, significantly improving feature extraction. By combining the energy of Shearlet subbands with features from previous techniques, our model simplifies feature representation, highlights key patterns, and remains robust to noise and transformations. Our proposed model has achieved impressive results on the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) dataset, with F1, Area Under the Curve (AUC), and Cohen's Kappa scores of 76.8 %, 84.5 %, and 50.9 %, respectively, outperforming other models.