Cancer is significant global health issue, resulting in millions of fatalities annually. Breast cancer, in particular, is highly common and remains a major cause of cancer-related death in women. Although there have been significant advancements in medical research and treatment options, the early detection and accurate classification of breast cancer are essential for improving patient outcomes. The intricate nature of cancer biology, along with large volume of clinical data, requires the use of advanced technologies to improve diagnostic accuracy and enable timely interventions.
In order to enable quick and precise breast cancer classification, we present a novel framework in this study that combines deep learning methods with imagining data from multiple modalities such as CT/MRI and PET scans. Early tests of the framework shows that it can accurately differentiate between benign and malignant tumours, indicating that it has the potential to be trustworthy tool for medical practitioners.
By using explainable AI techniques. The model’s transparency is further improved, enabling clinicians to comprehend the reasoning behind the predictions.