Review of AI & XAI-based breast cancer diagnosis methods using various imaging modalities

Breast cancer is a significant health concern; early detection and treatment are critical to improving patient outcomes. Artificial Intelligence has the potential to assist healthcare professionals in the diagnosis and treatment of breast cancer, and clinical decision support systems using AI algorithms can help to improve the accuracy and speed of breast cancer detection and diagnosis. By analysing large amounts of data from various imaging modalities, AI algorithms can help identify breast cancer early, increasing the chances of successful treatment. This survey aims to briefly overview the most prevalent breast cancer types, their staging, and the methods and modalities for their diagnosis to address these problems. Our investigation showed that, when compared to ML, DL techniques achieved the highest accuracy, with an approximate 2% improvement in accuracy. This review article mainly focuses on four commonly used imaging modalities: Mammogram, Thermogram, Ultrasound imaging and histopathology images. This survey critically analyzes breast cancer detection approaches and modalities, as well as a cost-to-accuracy comparison. Explainable AI (XAI) techniques and their application for breast cancer diagnosis are critically examined, as well as their advantages and limitations.