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@inproceedings{1764257,
author = {Maršálová, Kateřina and Schwarz, Daniel and Provaznik, Ivo},
address = {AMSTERDAM},
booktitle = {Digital Personalized Health and Medicine},
doi = {http://dx.doi.org/10.3233/SHTI200372},
editor = {Louise B. Pape-Haugaard; Christian Lovis; Inge Cort Madsen; Patrick Weber; Per Hostrup Nielsen; Philip Scott},
keywords = {Machine learning; neuroimaging; schizophrenia; support vector machines},
howpublished = {tištěná verze "print"},
language = {eng},
location = {AMSTERDAM},
isbn = {978-1-64368-082-8},
pages = {1221-1222},
publisher = {IOS PRESS},
title = {Classification of First-Episode Schizophrenia Using Wavelet Imaging Features},
url = {https://ebooks.iospress.nl/volumearticle/54374},
year = {2020}
}
TY - JOUR
ID - 1764257
AU - Maršálová, Kateřina - Schwarz, Daniel - Provaznik, Ivo
PY - 2020
TI - Classification of First-Episode Schizophrenia Using Wavelet Imaging Features
PB - IOS PRESS
CY - AMSTERDAM
SN - 9781643680828
KW - Machine learning
KW - neuroimaging
KW - schizophrenia
KW - support vector machines
UR - https://ebooks.iospress.nl/volumearticle/54374
N2 - 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.
ER -
MARŠÁLOVÁ, Kateřina, Daniel SCHWARZ a Ivo PROVAZNIK. Classification of First-Episode Schizophrenia Using Wavelet Imaging Features. In Louise B. Pape-Haugaard; Christian Lovis; Inge Cort Madsen; Patrick Weber; Per Hostrup Nielsen; Philip Scott. \textit{Digital Personalized Health and Medicine}. AMSTERDAM: IOS PRESS, 2020, s.~1221-1222. ISBN~978-1-64368-082-8. Dostupné z: https://dx.doi.org/10.3233/SHTI200372.
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