CANCERVISION: AUTOMATED BREAST CANCER DIAGNOSIS USING MULTI-MODAL IMAGING AND AI

Abstract

Breast cancer remains one of the leading causes of cancer-related deaths among women worldwide, making early detection and accurate diagnosis critical for effective treatment. This study presents CancerVision, an AI-driven diagnostic support system that integrates multi-modal imaging—including mammography, ultrasound, and MRI—to enhance the accuracy and reliability of breast cancer detection. By leveraging deep learning algorithms and image fusion techniques, CancerVision analyzes complex imaging data, identifies suspicious lesions, and provides automated diagnostic suggestions to assist radiologists in clinical decision-making. Experimental evaluations demonstrate that the system achieves high sensitivity, specificity, and diagnostic accuracy while reducing manual interpretation time and inter-observer variability. The study highlights the potential of combining multi-modal imaging with artificial intelligence to create a robust, scalable, and intelligent support system for early breast cancer detection, ultimately improving patient outcomes and clinical efficiency.