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This paper presents an overview of a computer- aided system for the detection of carcinomas in the prostate gland. The proposed system incorporates information from two different types of magnetic resonance images (MRIs), namely the T2-weighted morphological images and the T1-weighted dynamic contrast enhanced (DCE) images, to extract discriminative features that will be used in the training phase of a classification algorithm for the differentiation between malignant and benign tissue. The resulting feature vectors are also used for the assessment of new patient cases. The pattern recognition scheme is based on probabilistic neural networks (PNNs). The parameters of the PNNs are estimated using the expectation- maximization (EM) algorithm. The performance of the proposed computer-aided detection system is evaluated through training and testing on several patient cases, whose condition has been previously assessed through ultrasound-guided biopsy and MRI examination.