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Abstract

The breast cancer problem is becoming a major death of women in the world; clinical experience provides that the early detection and proper diagnosis can protect the patients from breast cancer problem.  The human vision is always accurate in detecting different changes in the intensity of the vision.  The visual representation of any object is used to provide the proper communication to each other.  This paper provides the segmentation after preprocessing then classification of the different types of breast cancer problems. The mammogram imaging can be analyzed by applying proper algorithms to segment and classify the stage of cancer images.  The preprocessing technique is important to get accurate segmentation of the mammogram image matrix.  The preprocessing can be divided into two parts.  First part of preprocessing is to filter the mammogram image to eliminate noses like salt and pepper noise, random noise and speckle noise.  After image de-noising, the second part of preprocessing is image enhancement.  The image enhancement is used to improve the contrast and brightness of the image.  The is the qualified image to segment the Region Of Interest (ROI). The ROI is further subjected to extract the data for data mining.  The data features are given to the support vector machine (SVM) classifier to classify whether given mammogram image is normal or abnormal and its stages.

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