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Analysis of Breast Cancer Using Tetrolet Transform Suvetha K1, Sultana Mazher2 1Research Scholar, AMET University, Chennai 2Professor, Presidency College, Chennai Online published on 24 October, 2017. Abstract Breast cancer is considered as a severe problem and general form of cancer that often affects woman. A tool called Computer Aided Diagnosis (CAD) can assist the doctors in the recognition of abnormalities in medical images. In this paper, we have classified the tumor from the mammogram images to normal and abnormal classes. First the Region of Interest (ROI) of the mammogram image undergoes the tetrolet transform and its output sent to feature extraction stage. The Gray level Cooccurence Matrix (GLCM) features are extracted from different levels of decomposition. Then the features are analyzed using the Support Vector Machine (SVM) classifier and is classified as normal and abnormal classes. Top Keywords Mammogram, Breast Cancer, Tetrolet, GLCM and Svmclassification. Top | |
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