ALGORITHMS FOR EVALUATING SYMPTOMS FROM MAMMOGRAPHIC IMAGES IN THE DETECTION OF BREAST CANCER

Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, making early and accurate diagnosis critical for improving survival rates. Mammography is the primary imaging modality for early detection; however, visual analysis can be challenging due to dense tissue overlap and subtle radiological signs, such as micro calcifications, mass lesions, and architectural distortions. This paper presents an automated algorithmic framework designed to evaluate and classify pathological symptoms from mammographic images.