INTELLIGENT BREAST CANCER DIAGNOSIS THROUGH DEEP LEARNING SEGMENTATION
Pragati Kumari, S Pavani, Raj Kumar · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2025
Breast cancer remains one of the most prevalent and deadly forms of cancer worldwide, necessitating innovative approaches for early detection and treatment. The advent of deep learning has revolutionized medical imaging, particularly in the field of breast cancer diagnosis. This research explores the application of deep learning techniques in breast cancer image processing and segmentation. Utilizing convolutional neural networks (CNNs), the study aims to enhance the accuracy and efficiency of identifying malignant tumors in ultrasound images and other breast imaging modalities. By leveraging large datasets and advanced image processing algorithms, deep learning models are trained to differentiate between healthy and cancerous tissues with high precision. This approach not only improves diagnostic capabilities but also aids in the segmentation of tumors, which is crucial for treatment planning and monitoring. The integration of deep learning in breast cancer detection holds significant promise for reducing false positives and negatives, ultimately contributing to better patient outcomes and personalized care. Keywords: Breast cancer, Classification, Segmentation, Artificial intelligence (AI), Machine learning, Deep learning, Texture analysis, Shape analysis