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Technological innovations have produced remarkable results in the health care sector. In particular, computer-aided detection (CAD) systems are becoming very useful and helpful in supporting physicians for early detection and control of some diseases such as neoplastic pathologies. In this paper, two different CAD systems able to detect and to localize microcalcification clusters in mammographic images are implemented. The two methods utilize an artificial neural network and a support vector machine, respectively, as classifier. Adopting the MIAS database as procedure testing, the performance of the two implemented systems are compared in terms of sensitivity, specificity, accuracy, free-response operating characteristic curves, and Cohen's kappa coefficient. The obtained values for the previous parameters show the efficiency of both methods to operate as “second opinion” in microcalcification cluster detection, improving the screening process efficiency.