Explainability in breast cancer detection

Breast cancer is one of the most prevalent and lethal conditions among women across the globe, requiring timely and accurate diagnosis to contribute to better patient outcomes. Recent studies explored the risk factors connected to breast cancer. In premenopausal women and those with BRCA genetic susceptibility, air pollution predisposes to breast cancer because environmental toxins are more capable of inducing harmful results in these vulnerable groups, particularly those residing in densely populated urban areas with elevated pollution concentrations, and in the neighborhood of construction sites. Recent decades have seen deep learning emerging as a general-purpose piece of computer-assisted diagnosis software, enabling classification and segmentation tasks in the domain of medical imaging. These models are particularly effective at detecting weak patterns within imaging data imperceptible to the human eye, drastically enhancing diagnostic efficiency. This article focuses on the task of breast cancer classification using ultrasound images. Our results pinpoint ResNet50 as the best-performing model, which has a remarkable 98.72% accuracy rate. We further interpret the model’s outcome using the XAI tool Grad-CAM by examining its capability to provide interpretable explanations. The XAI method provides clinically relevant and interpretable explanations, as supported by analysis using both the original images and their corresponding segmented masks.