Detection of breast cancer tissues in mammogram images using deep learning method

• Main Authors: Kamalapahthi, Ragunathan, Singarayan, Rajakumar, Somichetty, Uma Maheswari, Rajarethnam, Subraja
• Format: Conference Proceeding
• Language: English
• Subjects: Breast cancer; Calcification; Deep learning; Machine learning; Mammography; Medical imaging; Neural networks
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0165441

For many women with breast cancer, it is one of the most common and deadliest cancers in women; it is estimated that one in eight women will develop breast cancer during her lifetime. Early detection is important to improve their chances of survival: about 100% of cases diagnosed early survive the disease. Radiologists use mammograms, x-ray images of the breast, to look for signs of a possible tumor such as breast cancer, cancerous growths, and micro calcification, a small deposit of calcium that binds to abnormal tissue. In this work acquire without the outsourcing of network training but in the future aim to acquire more masses using the Probabilistic neural networks, a modern learning machine that enables image separation in one readable step. It will automatically detect breast cancer in mammograms that can be used to improve micro calcification (MC) detection. Signs of Mass detection and micro calcification sets are important for the earlydetection of breast cancer.