A study on mammography computer aided diagnosis system using machine learning methods

• Published in: IET Chennai Fourth International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2013) pp. 333 - 341
• Main Authors: Dinakaran, K, Sivakrithika, V
• Format: Conference Proceeding
• Language: English
• Published: Stevenage, UK IET 2013; The Institution of Engineering & Technology
• Subjects: Biology and medical computing; Computer vision and image processing techniques; Image recognition; Knowledge engineering techniques; Patient diagnostic methods and instrumentation; X-ray techniques: radiography and computed tomography (biomedical imaging/measurement); X-rays and particle beams (medical uses); biological organs; image processing; image classification; mammography; breast cancer treatment; data mining; machine learning methods; mammogram classification; malign; computerized biomedical image analysis; abnormality detection; benign; cancer; mammography computer aided diagnosis system; learning (artificial intelligence); radiologists; medical image processing; pattern recognition
• ISBN: 9781785610301; 1785610309
• DOI: 10.1049/ic.2013.0334

Early diagnosis is an important aspect of successful treatment for breast cancer. Mammogram is the most reliable imaging technique available. It is a challenging task for radiologists to detect the abnormalities in the mammograms. Computing helps the radiologists in diagnosing the abnormalities in the mammogram. Computer Aided Diagnosis System involves computerized biomedical image analysis to classify the mammography into benign or malign. In a decade of research work number of algorithms had been proposed to classify the image that employ data mining techniques, image processing methods, machine learning methods and pattern recognition. In this paper such algorithms in previous research work is studied and their performance is discussed.