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In the recent years the death rate due to breast cancer among women has increased significantly and now it is a recognized world health problem. Early detection and treatment can reduce the death rate of breast cancer effectively. Presently numbers of imaging techniques are available for detection of breast cancer. Mammography test is the most efficient and reliable to find the breast cancer early. But finding and detecting breast cancer on mammogram is repetitive, tiring and fatigue obligation to radiologist; hence sometimes it may be overlooked. Therefore, smart Computer-Aided Detection system require to be extended and combined in new way in order to provide automatic detection of suspicious mass that meets the needs of medical application to point out the occurrence of breast cancer. Suspicious mass detection accuracy can be improved, which will assist the radiologist to classify the breast cancer. This paper presents algorithms to detect the suspicious mass in mammogram image, and also extract GLCM features of suspicious mass. These extracted GLCM features are graphically represented and based on the variation of these features; the mammogram is analyzed and classified as malignant and non-malignant.
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