An effective two way classification of breast cancer images

Breast cancer is a highly heterogeneous disease and very common among western women. Mammogram is an examination of a woman’s breasts using X-rays to check for cancer. Mammography is one of the first diagnostic tests to prescreen breast cancer. Early detection of breast cancer has been known to improve recovery rates to a great extent. In most medical centers, experienced radiologists are given the responsibility of analyzing mammograms. But, there is always a possibility of human error. Errors can frequently occur as a result of fatigue of the observer, resulting in interobserver and intraobserver variations. The sensitivity of mammographic screening also varies with image quality. To offset different kinds of variability and to standardize diagnostic procedures, efforts are being made to develop automated techniques for diagnosis and grading of breast cancer images. This paper presents a two way classification algorithm for the classification of breast cancer images into benign (tumour growing, but not dangerous) and malignant (cannot be controlled, it causes death) classes. Because of the sparse distribution of abnormal mammograms, the two-way classification data mining algorithms are used. First classification algorithm is k-means algorithm which is used to partition a given dataset into a user specified number of clusters. Second classification algorithm is Support Vector Machine (SVM) is used to find the best classification function to distinguish between members of the two classes in the training data.