Detail publikace

Classification of First-Episode Schizophrenia Using Wavelet Imaging Features

MARŠÁLOVÁ, K. SCHWARZ, D. PROVAZNÍK, I.

Originální název

Classification of First-Episode Schizophrenia Using Wavelet Imaging Features

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Machine learning; neuroimaging; schizophrenia; support vector machines

Autoři

MARŠÁLOVÁ, K.; SCHWARZ, D.; PROVAZNÍK, I.

Vydáno

1. 5. 2020

Nakladatel

IOS Press

Místo

Geneve

ISBN

978-1-64368-083-5

Kniha

Digital Personalized Health and Medicine

Edice

Studies in Health Technology and Informatics

Strany od

1221

Strany do

1222

Strany počet

2

URL

http://ebooks.iospress.nl/publication/54374

BibTex

@inproceedings{BUT167774,
  author="Kateřina {Maršálová} and Daniel {Schwarz} and Valentine {Provazník}",
  title="Classification of First-Episode Schizophrenia Using Wavelet Imaging Features",
  booktitle="Digital Personalized Health and Medicine",
  year="2020",
  series="Studies in Health Technology and Informatics",
  pages="1221--1222",
  publisher="IOS Press",
  address="Geneve",
  doi="10.3233/SHTI200372",
  isbn="978-1-64368-083-5",
  url="http://ebooks.iospress.nl/publication/54374"
}