Multimodal schizophrenia detection by multiclassification analysis

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 %.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages491-498
Number of pages8
Volume7042 LNCS
DOIs
Publication statusPublished - 2011
Event16th Iberoamerican Congress on Pattern Recognition, CIARP 2011 - Pucon, Chile
Duration: Nov 15 2011Nov 18 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7042 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other16th Iberoamerican Congress on Pattern Recognition, CIARP 2011
CountryChile
CityPucon
Period11/15/1111/18/11

Keywords

  • Correlation
  • Machine learning algorithms
  • Magnetic resonance imaging
  • Support vector machines

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Ulaş, A., Castellani, U., Mirtuono, P., Bicego, M., Murino, V., Cerruti, S., Bellani, M., Atzori, M., Rambaldelli, G., Tansella, M., & Brambilla, P. (2011). Multimodal schizophrenia detection by multiclassification analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7042 LNCS, pp. 491-498). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7042 LNCS). https://doi.org/10.1007/978-3-642-25085-9_58