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.

In this paper, a Computer Aided System for microcalcification cluster diagnosis in mammographic images is presented. The method is characterized by three phases. In fact, single microcalcifications are first localized then microcalcifications having a cluster pattern are detected. In the last step the procedure classifies the abnormalities as benign or malignant microcalcification clusters. Features extracted from localized single microcalcifications are fed into a Support Vector Machine classifier to verify the presence of microcalcification cluster, minimizing false positive detections. For the diagnosis purpose, an Artificial Neural Network classifier is implemented which makes use of features extracted from previously detected microcalcification clusters as inputs. The performance of the implemented system is evaluated taking into account the accuracy of both detecting and classifying microcalcification clusters. Adopting the MIAS database as test bench, a sensitivity of about 98.4 at a rate of 0.85 FP/image is achieved in detecting microcalcification clusters. Moreover, the method gets a sensitivity of about 93.5% and an accuracy value equal to 94.2% in classifying the detected microcalcification clusters. The obtained system performance shows its ability of aiding the interpretation of specialists and, consequently, it could be considered as a second opinion method.