INTEGRATING AI WITH MULTI-MODAL IMAGING FOR EARLY AND ACCURATE BREAST CANCER DETECTION

Abstract

Breast cancer continues to be a major cause of cancerrelated mortality among women worldwide. Timely identification significantly enhances the likelihood of effective therapy and prolonged life. Conventional diagnostic techniques mostly depend on singlemodality imaging, such mammography, which may fail to encompass the whole spectrum of tumour features. This work presents a sophisticated diagnostic assistance system that amalgamates artificial intelligence (AI) with multi-modal imaging techniques—namely mammography, ultrasound, and magnetic resonance imaging (MRI)—to improve the precision and dependability of breast cancer detection. The system employs deep learning models trained on diverse picture datasets to autonomously identify malignancies, categorise tumour kinds, and minimise false positives. The use of AI enhances clinical processes and offers radiologists a robust decisionsupport tool that facilitates early and accurate diagnosis, thereby improving patient outcomes.