D 2020

Classification of First-Episode Schizophrenia Using Wavelet Imaging Features

MARŠÁLOVÁ, Kateřina, Daniel SCHWARZ and Ivo PROVAZNIK

Basic information

Original name

Classification of First-Episode Schizophrenia Using Wavelet Imaging Features

Authors

MARŠÁLOVÁ, Kateřina (203 Czech Republic, guarantor, belonging to the institution), Daniel SCHWARZ (203 Czech Republic, belonging to the institution) and Ivo PROVAZNIK (203 Czech Republic)

Edition

AMSTERDAM, Digital Personalized Health and Medicine, p. 1221-1222, 2 pp. 2020

Publisher

IOS PRESS

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Netherlands

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

References:

RIV identification code

RIV/00216224:14110/20:00118546

Organization unit

Faculty of Medicine

ISBN

978-1-64368-082-8

ISSN

DOI

UT WoS

000625278800255

Keywords in English

Machine learning; neuroimaging; schizophrenia; support vector machines

Tags

Tags

International impact, Reviewed
Změněno: 12/5/2021 14:53, Mgr. Tereza Miškechová

Abstract

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.