Breast Cancer is a fatal disease. Several people are losing their lives as a result of Breast Cancer. Mammography is the most often used Breast screening modality where we can see both mass and microcalcifications and both are the two major indicators of Breast Cancer. We can see Pectoral muscle also on MLO Mammograms. Digital Image Processing based computer aided diagnosis systems are being used widely to help the radiologist in detecting mass and microcalcifications in MLO Mammograms. However, because the intensity levels of the Pectoral muscle are similar to masses, in computer aided diagnosis system, Pectoral presence in the Mammogram has a detrimental effect on identifying mass. Therefore, in computer aided diagnosis system, Pectoral muscle masking substantially enhances lesion detection. This study suggests a novel ensemble computer aided diagnosis system strategy that combines the MSER based and SlopeEdgeDetection methods with extensive pre-processing to identify and cover Pectoral muscle from MLO Mammograms. The results demonstrate that the new procedure is straightforward and improves the precision of Pectoral region covering. Compared to the average accuracy of the state-of-the-art solutions which is 94%, the suggested technique achieves an accuracy of 99%. Performance analysis makes use of the Mini-MIAS database
