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In this paper, a novel Computer-aided Diagnosis (CADx) system has been proposed for mass diagnosis in mammography images. Zernike moments are utilized as descriptors of shape and density characteristics in order to improve the overall accuracy. The input Regions of Interest (ROI) are segmented and subjected to some preprocessing stages. The outcome of preprocessing stage is a gray-scale image containing co-scaled translated mass which contains both shape and density characteristics of the mass. Two groups of Zernike moments have been extracted from the preprocessed images. Considering the performance of the overall system the most effective moments have been chosen and applied to a Multi-layer Perceptron (MLP) classifier. The Receiver Operational Characteristics (ROC) plot and the performance of overall CADx system are analyzed for each group of features. The average achieved area under ROC curve (Az) and False Positive Rate (FPR) for high-order moments are 0.872 and 18.34%, respectively. Besides, for low-order moments those are equal to 0.824 and 15.44%, respectively.