Multimodal schizophrenia detection by multiclassification analysis

  • Authors:

  • Aydın Ulaş;Umberto Castellani;Pasquale Mirtuono;Manuele Bicego;Vittorio Murino;Stefania Cerruti;Marcella Bellani;Manfredo Atzori;Gianluca Rambaldelli;Michele Tansella;Paolo Brambilla

  • Affiliations:

  • Department of Computer Science, University of Verona, Verona, Italy;Department of Computer Science, University of Verona, Verona, Italy;Department of Computer Science, University of Verona, Verona, Italy;Department of Computer Science, University of Verona, Verona, Italy;Department of Computer Science, University of Verona, Verona, Italy;Department of Public Health and Community Medicine, Section of Psychiatry and Clinical Psychology, Inter-University Centre for Behavioural Neurosciences, University of Verona, Verona, Italy;Department of Public Health and Community Medicine, Section of Psychiatry and Clinical Psychology, Inter-University Centre for Behavioural Neurosciences, University of Verona, Verona, Italy;IRCCS “E. Medea” Scientific Institute, Udine, Italy;Department of Public Health and Community Medicine, Section of Psychiatry and Clinical Psychology, Inter-University Centre for Behavioural Neurosciences, University of Verona, Verona, Italy;Department of Public Health and Community Medicine, Section of Psychiatry and Clinical Psychology, Inter-University Centre for Behavioural Neurosciences, University of Verona, Verona, Italy;IRCCS “E. Medea” Scientific Institute, Udine, Italy

  • Venue:
  • CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
  • Year:
  • 2011

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Abstract

We propose a multiclassification analysis to evaluate the relevance of different factors in schizophrenia detection. Several Magnetic Resonance Imaging (MRI) scans of brains are acquired from two sensors: morphological and diffusion MRI. Moreover, 14 Region Of Interests (ROIs) are available to focus the analysis on specific brain subparts. All information is combined to train three types of classifiers to distinguish between healthy and unhealthy subjects. Our contribution is threefold: (i) the classification accuracy improves when multiple factors are taken into account; (ii) proposed procedure allows the selection of a reduced subset of ROIs, and highlights the synergy between the two modalities; (iii) correlation analysis is performed for every ROI and modality to measure the information overlap using the correlation coefficient in the context of schizophrenia classification. We see that we achieve 85.96 % accuracy when we combine classifiers from both modalities, whereas the highest performance of a single modality is 78.95 %.