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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • 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

  • 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