TY - GEN
T1 - Multimodal schizophrenia detection by multiclassification analysis
AU - Ulaş, Aydin
AU - Castellani, Umberto
AU - Mirtuono, Pasquale
AU - Bicego, Manuele
AU - Murino, Vittorio
AU - Cerruti, Stefania
AU - Bellani, Marcella
AU - Atzori, Manfredo
AU - Rambaldelli, Gianluca
AU - Tansella, Michele
AU - Brambilla, Paolo
PY - 2011
Y1 - 2011
N2 - 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 %.
AB - 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 %.
KW - Correlation
KW - Machine learning algorithms
KW - Magnetic resonance imaging
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=81855226096&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=81855226096&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-25085-9_58
DO - 10.1007/978-3-642-25085-9_58
M3 - Conference contribution
AN - SCOPUS:81855226096
SN - 9783642250842
VL - 7042 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 491
EP - 498
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 16th Iberoamerican Congress on Pattern Recognition, CIARP 2011
Y2 - 15 November 2011 through 18 November 2011
ER -