Mammography images classification system based texture analysis and multi class support vector machine.

Breast cancer is the greatest common reason of loss women in the world and the additional important cause of cancer losses world-wide. Classification and Detection of breast cancer are very significant since it offers body information of abnormal and normal soft tissue which supports in primary treatment planning and patient's situation follow-up, which is critical for woman's excellence in her life. X-ray mammography is the chief check used within quick diagnosis and screening, mammography is using in the medical imaging, and its exploration and processing are thesolutions for improving this tumor or cancer prognosis, several computer_aided finding structures have been advanced to provide support radiologists and internists for their diagnosis. In this article, an method is proposed to efficiently analyze digital mammograms based on texture segmentation to the detection for first stage tumors and there are a number of methods for medical image classification. The proposed algorithm was Multi Class Support Vector Machine and system accuracy of (98%). [ABSTRACT FROM AUTHOR]