Explainable AI for Breast Cancer Detection in Mammograms using Vision Transformers and Grad- CAM++

Breast cancer is the major causes of death among women in the world and early diagnosis using mammography is important in enhancing the clinical outcome. Manual interpretation of mammograms is not easy because of noise of the images, dense breast tissue, appearance of lesions and inter-observer variation across radiologists. In a bid to overcome these shortcomings, this research introduces an explainable Artificial Intelligence (AI) framework of automated breast cancer detection based on Vision Transformers (ViT) with Grad-CAM++ to provide visual intelligibility. The given method utilizes conventional preprocessing to a mammographic image with transformer-based learning. In comparison to the traditional convolutional neural networks where the main task is to capture local spatial features, Vision Transformer utilizes self-attention networks to learn long-range contextual correlation over the entire image, which is more effective in detecting diffuse and small regions of malignancy. Grad-CAM++ is used during inference to produce fine-grained heatmaps highlighting image regions that are helpful in the model predictions to increase transparency and clinical trust. The system is trained in a monitored fashion by the means of labeled mammography data and tested by standard classification measures, showing consistent learning and high forecasting accuracy. The resulting framework offers binary classification of the cancer types as well as visual explanation of the model decisions which can be used to validate the model decisions by the radiologist. In general, the combination of transformerbased global feature modeling with state-of-the-art explainability methods provides a stable, interpretable, and reproducible mechanism of automated mammogram analysis that satisfies the needs of clinical decision support and research settings.