We propose to approach the detection of patients affected by schizophrenia by means of dissimilarity-based classification techniques applied to brain magnetic resonance images. Instead of working with features directly, pairwise distances between expert delineated regions of interest (ROIs) are considered as representations based on which learning and classification can be performed. Experiments were carried out on a set of 64 patients and 60 controls and several pairwise dissimilarity measurements have been analyzed. We demonstrate that good results are possible and especially significant improvements can be obtained when combining over different ROIs and different distance measures. The lowest error rate obtained is 0.210.
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