Breast cancer is one of the leading causes of death among women worldwide. Early and accurate detection plays a vital role in improving survival rates and guiding effective treatment. In this study, we propose a deep learning-based model for automatic breast cancer detection using mammogram images. The model is divided into three phases: preprocessing, segmentation, and classification. The first two phases - image enhancement and segmentation were developed and validated in our previous works. In this paper, we focus on the classification phase and introduce a novel hybrid model that combines the strengths of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). This model captures both fine image details and global context, making it highly effective for distinguishing between benign and malignant breast tumors. We also include attention-based feature fusion and Grad-CAM visualizations to make predictions more interpretable for clinical use. The model was tested on multiple benchmark datasets—DDSM, INbreast, and MIAS—and achieved excellent results, including 100% accuracy on MIAS and over 99% accuracy on other datasets. Compared to recent deep learning models, our method outperforms existing approaches in both accuracy and reliability. This research offers a promising step toward supporting radiologists with intelligent tools that can improve the speed and accuracy of breast cancer diagnosis.
Saini et al. (Thu,) studied this question.