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：2017
卷号：95
期号：17
页码：4059
出版社：Journal of Theoretical and Applied
摘要：The purpose of the present study is to extract pattern texture from regions of interest (ROI) on mammograms and to use texture descriptors to classify the ROI into benign or malignant mammograms. Supervised Machine Learning (SML) algorithms like Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) are used to classify the ROI. Two types of texture descriptors (GLCM and GRLM) are extracted after cropping and resizing the ROI. The goal is to find the best texture descriptors which give best accuracy in the classification of mammogrames. Our proposed method is proved to be a highly efficient method for the diagnostic of breast cancer with high accuracy using SVM. This study proves that SVM is a consistent classifier for two mammogram databases use.
