Classification of Mammogram Images by Using SVM and KNN

Breast cancer is a fairly diverse illness that affects large percentage of women in the west. A mammogram an X-ray-based evaluation woman's breasts to see if she has cancer. One earliest prescreening diagnostic procedures for breast mammography. It well known recovery rates are significantly increased by early identification. Mammogram analysis typically delegated skilled radiologists at medical facilities. Human mistake, however, always possibility. Fatigue observer can commonly lead errors, resulting intraobserver and interobserver variances. The image quality sensitivity mammographic screening as well. goal developing automated techniques detection grading images reduce various types variability standardize procedures. classification into benign (tumor increasing, but not harmful) malignant (cannot be managed, it causes death) classes using two-way algorithm shown this study. data mining algorithms utilized because there many abnormal mammograms. first algorithm, k-means, divides given dataset predetermined number clusters. Support Vector Machine (SVM), second used identify optimal function separate members two training