Brain Morphometry Methods for Feature Extraction in Random Subspace Ensemble Neural Network Classification of First-Episode Schizophrenia
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F19%3A00108524" target="_blank" >RIV/00216224:14110/19:00108524 - isvavai.cz</a>
Alternative codes found
RIV/65269705:_____/19:00071122
Result on the web
<a href="http://dx.doi.org/10.1162/neco_a_01180" target="_blank" >http://dx.doi.org/10.1162/neco_a_01180</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1162/neco_a_01180" target="_blank" >10.1162/neco_a_01180</a>
Alternative languages
Result language
angličtina
Original language name
Brain Morphometry Methods for Feature Extraction in Random Subspace Ensemble Neural Network Classification of First-Episode Schizophrenia
Original language description
Machine learning (ML) is a growing field that provides tools for automatic pattern recognition. The neuroimaging community currently tries to take advantage of ML in order to develop an auxiliary diagnostic tool for schizophrenia diagnostics. In this letter, we present a classification framework based on features extracted from magnetic resonance imaging (MRI) data using two automatic whole-brain morphometry methods: voxel-based (VBM) and deformation-based morphometry (DBM). The framework employs a random subspace ensemble-based artificial neural network classifier-in particular, a multilayer perceptron (MLP). The framework was tested on data from first-episode schizophrenia patients and healthy controls. The experiments differed in terms of feature extraction methods, using VBM, DBM, and a combination of both morphometry methods. Thus, features of different types were available for model adaptation. As we expected, the combination of features increased the MLP classification accuracy up to 73.12%-an improvement of 5% versus MLP-based only on VBM or DBM features. To further verify the findings, other comparisons using support vector machines in place of MLPs were made within the framework. However, it cannot be concluded that any classifier was better than another.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30103 - Neurosciences (including psychophysiology)
Result continuities
Project
<a href="/en/project/NV17-33136A" target="_blank" >NV17-33136A: Neurominer: unveiling hidden patterns in neuroimaging data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Computation
ISSN
0899-7667
e-ISSN
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Volume of the periodical
31
Issue of the periodical within the volume
5
Country of publishing house
US - UNITED STATES
Number of pages
22
Pages from-to
897-918
UT code for WoS article
000476941800004
EID of the result in the Scopus database
2-s2.0-85064454308