Breast cancer is a serious medical condition that requires early detection for successful treatment. Mammography is a commonly used imaging technique for breast cancer screening, but its analysis can be time-consuming and subjective. This study explores the use of deep learning-based methods for mammogram analysis, with a focus on improving the performance of the analysis process. The study is focused on applying different computer vision models, with both CNN and ViT architectures, on a publicly available dataset. The innovative approach is represented by the data augmentation technique based on synthetic images, which are generated to improve the performance of the models. The results of the study demonstrate the importance of data pre-processing and augmentation techniques for achieving high classification performance. Additionally, the study utilizes explainable AI techniques, such as class activation maps and centered bounding boxes, to better understand the models’ decision-making process.