Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F15%3A00087443" target="_blank" >RIV/00216224:14110/15:00087443 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.pscychresns.2015.03.004" target="_blank" >http://dx.doi.org/10.1016/j.pscychresns.2015.03.004</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.pscychresns.2015.03.004" target="_blank" >10.1016/j.pscychresns.2015.03.004</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition

Popis výsledku v původním jazyce

We investigated a combination of three classification algorithms, namely the modified maximum uncertainty linear discriminant analysis (mMLDA), the centroid method, and the average linkage, with three types of features extracted from three-dimensional T1-weighted magnetic resonance (MR) brain images, specifically MR intensities, grey matter densities, and local deformations for distinguishing 49 first episode schizophrenia male patients from 49 healthy male subjects. The feature sets were reduced using intersubject principal component analysis before classification. By combining the classifiers, we were able to obtain slightly improved results when compared with single classifiers. The best classification performance (81.6% accuracy, 75.5% sensitivity, and 87.8% specificity) was significantly better than classification by chance.

Název v anglickém jazyce

Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition

Popis výsledku anglicky

We investigated a combination of three classification algorithms, namely the modified maximum uncertainty linear discriminant analysis (mMLDA), the centroid method, and the average linkage, with three types of features extracted from three-dimensional T1-weighted magnetic resonance (MR) brain images, specifically MR intensities, grey matter densities, and local deformations for distinguishing 49 first episode schizophrenia male patients from 49 healthy male subjects. The feature sets were reduced using intersubject principal component analysis before classification. By combining the classifiers, we were able to obtain slightly improved results when compared with single classifiers. The best classification performance (81.6% accuracy, 75.5% sensitivity, and 87.8% specificity) was significantly better than classification by chance.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

FH - Neurologie, neurochirurgie, neurovědy

OECD FORD obor

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Projekt

<a href="/cs/project/NT13359" target="_blank" >NT13359: Pokročilé metody rozpoznávání MR obrazů mozku pro podporu diagnostiky neuropsychiatrických poruch</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Psychiatry Research: Neuroimaging

ISSN

0925-4927

e-ISSN

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Svazek periodika

232

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

IE - Irsko

Počet stran výsledku

13

Strana od-do

237-249

Kód UT WoS článku

000354552900006

EID výsledku v databázi Scopus

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