MAMMOGRAM CLASSIFICATION USING MAXIMUM DIFFERENCE FEATURE SELECTION METHOD
R. Nithya, B. Santhi · 2011
This paper developed a CAD (Computer Aided Diagnosis) system based on neural network and a proposed feature selection method. The proposed feature sele ction method is Maximum Difference Feature Selection (MDFS). Digital mammography is reliable method for early detection of breast cancer. The most importan t step in breast cancer diagnosis is feature selectio n. Computer automated feature selection is reliable and also it helps to improve the classification accurac y. GLCM (Gray Level Co-occurrence Matrix) features are extracted from the mammogram. The extracted features are selected based on a proposed MDFS method. Experiments have been conducted on datasets from DDSM (Digital database for Screening Mammography) database. Several feature selection methods are ava ilable. The accuracy of the model depends on the relevant feature selection. The proposed MDFS method selects only essential features and eliminates th e irrelevant features. The experiment results show th at neural network based model with proposed feature selection method improved the classification accura cy .