Classification of First-Episode Schizophrenia Using Wavelet Imaging Features

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Classification of First-Episode Schizophrenia Using Wavelet Imaging Features

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Classification of First-Episode Schizophrenia Using Wavelet Imaging Features

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Digital Personalized Health and Medicine

ISBN

9781643680828

ISSN

0926-9630

e-ISSN

—

Počet stran výsledku

2

Strana od-do

1221-1222

Název nakladatele

IOS PRESS

Místo vydání

AMSTERDAM

Místo konání akce

Geneva

Datum konání akce

1. 1. 2020

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

000625278800255