ENHANCING DIAGNOSTIC ACCURACY AND EARLY DETECTION IN BREAST CANCER MAMMOGRAPHY THROUGH DEEP LEARNING-BASED AI

Deep learning-based artificial intelligence has increasingly become an integral component in breast cancer
mammography, offering advancements in both diagnostic accuracy and the timeliness of early detection. As
computational models continue to improve, these systems are capable of processing complex imaging data and
identifying malignancies that might otherwise escape human observation. Through automated analysis, AI tools
support radiologists in distinguishing between benign and malignant findings, thereby reducing diagnostic errors
and contributing to more reliable interpretations. Moreover, the integration of AI-driven approaches has shown
the capacity to identify subtle features in mammograms that correlate with early-stage disease, ultimately
supporting earlier intervention and improved patient outcomes. Collectively, these developments illustrate the
transformative potential of deep learning-based AI to augment traditional diagnostic processes in breast cancer
screening programs