A Novel Algorithm for the Automatic Detection and Classification of Microcalcification Clusters Using Wavelets

In the cancerous women society breast cancer can be found to be Second harmful cancer which causes death. According to statistical data the death rates are increasing in every year. Early detection and removal of the cancerous part is the most effective way to heal a cancer which can improve survival rates to a large level. Breast abnormality is find out with the distinguish features which can be simply misconstrue or missed by the radiologist. Again the mammographic screening sensitivity will vary with respect to the quality of image and ability of the radiologist. This means that there are no effective ways for the screening process. By early detecting the presence of micro calcification in the mammograms we can diagnose the breast cancer. This paper is to develop a novel algorithm for distinguishing benign clusters and malignant clusters. The proposed classification system reduces the classification errors and is further proficient in correct diagnosis which will be confirmed by experimental results.