A CAD system for the detection and classification of abnormalities in dense mammograms using electromagnetism-like optimization algorithm

The detection of abnormalities in the breast at an early stage can be so helpful for breast cancer treatment. Currently, mammography is the cheapest and the most efficient technique in terms of identifying the suspicious lesions in the breast. However, the interpretation of this screening remains so hard and could lead to inaccurate detection known as false positive and false negative. Dense breast category mammograms particularly, are difficult to read, because it may contain abnormal structures that are similar to the normal breast tissue. In this paper, we introduce an effecient Computer-Aided-Diagnosis system for the detection and classification of the ambiguous areas in dense breast mammograms. After noise and artifacts removal from the images using 2D Median filtering and labeling, we isolate the abnormalities using the metaheuristic algorithm Electromagnetism-like Optimization (EML), then we extract shape-based descriptors from the region of interest(ROI) using Zernike Moments. The detected abormal regions were classified into normal and abnormal based on the extracted shape features and through the Support Vector Machine(SVM) classification.