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Classification of First-Episode Schizophrenia Using Wavelet Imaging Features

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F20%3A00118546" target="_blank" >RIV/00216224:14110/20:00118546 - isvavai.cz</a>

  • Result on the web

    <a href="https://ebooks.iospress.nl/volumearticle/54374" target="_blank" >https://ebooks.iospress.nl/volumearticle/54374</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3233/SHTI200372" target="_blank" >10.3233/SHTI200372</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Classification of First-Episode Schizophrenia Using Wavelet Imaging Features

  • Original language description

    This work explores the design and implementation of an algorithm for the classification of magnetic resonance imaging data for computer-aided diagnosis of schizophrenia. Features for classification were first extracted using two morphometric methods: voxel-based morphometry (VBM) and deformation-based morphometry (DBM). These features were then transformed into a wavelet domain using the discrete wavelet transform with various numbers of decomposition levels. The number of features was then reduced by thresholding and subsequent selection by: Fisher's Discrimination Ratio (FDR), Bhattacharyya Distance, and Variances (Var.). A Support Vector Machine with a linear kernel was used for classification. The evaluation strategy was based on leave-one-out cross-validation.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Digital Personalized Health and Medicine

  • ISBN

    9781643680828

  • ISSN

    0926-9630

  • e-ISSN

  • Number of pages

    2

  • Pages from-to

    1221-1222

  • Publisher name

    IOS PRESS

  • Place of publication

    AMSTERDAM

  • Event location

    Geneva

  • Event date

    Jan 1, 2020

  • Type of event by nationality

    CST - Celostátní akce

  • UT code for WoS article

    000625278800255