Classification of First-Episode Schizophrenia Using Wavelet
Imaging Features

D 2020

Classification of First-Episode Schizophrenia Using Wavelet Imaging Features

MARŠÁLOVÁ, Kateřina, Daniel SCHWARZ a Ivo PROVAZNIK

Základní údaje

Originální název

Classification of First-Episode Schizophrenia Using Wavelet Imaging Features

Autoři

MARŠÁLOVÁ, Kateřina (203 Česká republika, garant, domácí), Daniel SCHWARZ (203 Česká republika, domácí) a Ivo PROVAZNIK (203 Česká republika)

Vydání

AMSTERDAM, Digital Personalized Health and Medicine, od s. 1221-1222, 2 s. 2020

Nakladatel

IOS PRESS

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

URL

Kód RIV

RIV/00216224:14110/20:00118546

Organizační jednotka

Lékařská fakulta

ISBN

978-1-64368-082-8

ISSN

DOI

http://dx.doi.org/10.3233/SHTI200372

UT WoS

000625278800255

Klíčová slova anglicky

Machine learning; neuroimaging; schizophrenia; support vector machines

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 12. 5. 2021 14:53, Mgr. Tereza Miškechová

Anotace

V originále

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.

Citovat

MARŠÁLOVÁ, Kateřina, Daniel SCHWARZ a Ivo PROVAZNIK. Classification of First-Episode Schizophrenia Using Wavelet Imaging Features. In Louise B. Pape-Haugaard; Christian Lovis; Inge Cort Madsen; Patrick Weber; Per Hostrup Nielsen; Philip Scott. Digital Personalized Health and Medicine. AMSTERDAM: IOS PRESS, 2020, s. 1221-1222. ISBN 978-1-64368-082-8. Dostupné z: https://dx.doi.org/10.3233/SHTI200372.

@inproceedings{1764257,
   author = {Maršálová, Kateřina and Schwarz, Daniel and Provaznik, Ivo},
   address = {AMSTERDAM},
   booktitle = {Digital Personalized Health and Medicine},
   doi = {http://dx.doi.org/10.3233/SHTI200372},
   editor = {Louise B. Pape-Haugaard; Christian Lovis; Inge Cort Madsen; Patrick Weber; Per Hostrup Nielsen; Philip Scott},
   keywords = {Machine learning; neuroimaging; schizophrenia; support vector machines},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {AMSTERDAM},
   isbn = {978-1-64368-082-8},
   pages = {1221-1222},
   publisher = {IOS PRESS},
   title = {Classification of First-Episode Schizophrenia Using Wavelet Imaging Features},
   url = {https://ebooks.iospress.nl/volumearticle/54374},
   year = {2020}
}

TY  - JOUR
ID  - 1764257
AU  - Maršálová, Kateřina - Schwarz, Daniel - Provaznik, Ivo
PY  - 2020
TI  - Classification of First-Episode Schizophrenia Using Wavelet Imaging Features
PB  - IOS PRESS
CY  - AMSTERDAM
SN  - 9781643680828
KW  - Machine learning
KW  - neuroimaging
KW  - schizophrenia
KW  - support vector machines
UR  - https://ebooks.iospress.nl/volumearticle/54374
N2  - 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.
ER  -

MARŠÁLOVÁ, Kateřina, Daniel SCHWARZ a Ivo PROVAZNIK. Classification of First-Episode Schizophrenia Using Wavelet Imaging Features. In Louise B. Pape-Haugaard; Christian Lovis; Inge Cort Madsen; Patrick Weber; Per Hostrup Nielsen; Philip Scott. \textit{Digital Personalized Health and Medicine}. AMSTERDAM: IOS PRESS, 2020, s.~1221-1222. ISBN~978-1-64368-082-8. Dostupné z: https://dx.doi.org/10.3233/SHTI200372.

Podrobný výpis o publikaci