期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2011
卷号：33
期号：2
页码：197-204
出版社：Journal of Theoretical and Applied
摘要：This paper developed a CAD (Computer Aided Diagnosis) system based on neural network and a proposed feature selection method. The proposed feature selection method is Maximum Difference Feature Selection (MDFS). Digital mammography is reliable method for early detection of breast cancer. The most important step in breast cancer diagnosis is feature selection. Computer automated feature selection is reliable and also it helps to improve the classification accuracy. 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 available. The accuracy of the model depends on the relevant feature selection. The proposed MDFS method selects only essential features and eliminates the irrelevant features. The experiment results show that neural network based model with proposed feature selection method improved the classification accuracy.
关键词：Artificial Neural Network (ANN); Breast Cancer; GLCM; Mammogram; Feature Selection
