Wavelet Features for Recognition of First Episode of Schizophrenia from MRI Brain Images
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Date
2014-04
ORCID
Advisor
Referee
Mark
Journal Title
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Volume Title
Publisher
Společnost pro radioelektronické inženýrství
Abstract
Machine learning methods are increasingly used in various fields of medicine, contributing to early diagnosis and better quality of care. These outputs are particularly desirable in case of neuropsychiatric disorders, such as schizophrenia, due to the inherent potential for creating a new gold standard in the diagnosis and differentiation of particular disorders. This paper presents a scheme for automated classification from magnetic resonance images based on multiresolution representation in the wavelet domain. Implementation of the proposed algorithm, utilizing support vector machines classifier, is introduced and tested on a dataset containing 104 patients with first episode schizophrenia and healthy volunteers. Optimal parameters of different phases of the algorithm are sought and the quality of classification is estimated by robust cross validation techniques. Values of accuracy, sensitivity and specificity over 71% are achieved.
Description
Citation
Radioengineering. 2014, vol. 23, č. 1, s. 274-281. ISSN 1210-2512
http://www.radioeng.cz/fulltexts/2014/14_01_0274_0281.pdf
http://www.radioeng.cz/fulltexts/2014/14_01_0274_0281.pdf
Document type
Peer-reviewed
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Published version
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en