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In this paper, we perform the assessment of a CAD for the tumoral masses classification in mammograms by the uncertainty propagation through the system. Carrying on the work of the authors concerning the metrological characterization of the developed CAD, we validate the features extraction, features selection, and classification steps in this paper. In particular, suitable metrics such as the Receiving Operating Curve (ROC) and the Area Under ROC (AUC) are widely used in order to provide a quantitative evaluation of the performance. Finally, we implement a Monte Carlo simulation in order to provide the confidence interval for some coverage probabilities for all involved parameters. The procedure is tested on mammographic images containing both malignant and benign breast masses.