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：220-226
出版社：Journal of Theoretical and Applied
摘要：This paper presents an evaluation and comparison of the performance of three different feature extraction methods for classification of normal and abnormal patterns in mammogram. Three different feature extraction methods used here are intensity histogram, GLCM (Grey Level Co-occurrence Matrix) and intensity based features. A supervised classifier system based on neural network is used. The performance of the each feature extraction method is evaluated on Digital Database for Screening Mammography (DDSM) breast cancer database. The experimental results suggest that GLCM method outperformed the other two methods
关键词：Artificial Neural Network (ANN); Breast Cancer; GLCM; Histogram; Intensity; Feature
