Classification of First-Episode Schizophrenia Using Wavelet Imaging Features
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Classification of First-Episode Schizophrenia Using Wavelet Imaging Features
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Digital Personalized Health and Medicine
ISBN
9781643680828
ISSN
0926-9630
e-ISSN
—
Number of pages
2
Pages from-to
1221-1222
Publisher name
IOS PRESS
Place of publication
AMSTERDAM
Event location
Geneva
Event date
Jan 1, 2020
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
000625278800255