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

Abstract

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.

Computer‐aided mammogram diagnosis system using deep learning convolutional fully complex‐valued relaxation neural network classifier

“…The identification of breast abnormalities, such as masses and microcalcifications, on mammographic images is a challenging task, particularly for dense breasts. Computer Aided Diagnosis (CAD) has been evolving over the years for early detection and analysis of breast cancers [4, 5]. The overall efficiency of a CAD process depends, to a large extent, on the accuracy with which the tumours in mammogram are segmented.…”