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@article{1181736, author = {Dluhoš, Petr and Schwarz, Daniel and Kašpárek, Tomáš}, article_number = {1}, keywords = {schizophrenia; machine learning; neuroimaging; classification; wavelet transform; MRI}, language = {eng}, issn = {1210-2512}, journal = {Radioengineering}, title = {Wavelet Features for Recognition of First Episode of Schizophrenia from MRI Brain Images}, url = {http://www.radioeng.cz/fulltexts/2014/14_01_0274_0281.pdf}, volume = {23}, year = {2014} }
TY - JOUR ID - 1181736 AU - Dluhoš, Petr - Schwarz, Daniel - Kašpárek, Tomáš PY - 2014 TI - Wavelet Features for Recognition of First Episode of Schizophrenia from MRI Brain Images JF - Radioengineering VL - 23 IS - 1 SP - 274-281 EP - 274-281 PB - SPOLECNOST PRO RADIOELEKTRONICKE INZENYRSTVI SN - 12102512 KW - schizophrenia KW - machine learning KW - neuroimaging KW - classification KW - wavelet transform KW - MRI UR - http://www.radioeng.cz/fulltexts/2014/14_01_0274_0281.pdf N2 - 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. ER -
DLUHOŠ, Petr, Daniel SCHWARZ a Tomáš KAŠPÁREK. Wavelet Features for Recognition of First Episode of Schizophrenia from MRI Brain Images. \textit{Radioengineering}. SPOLECNOST PRO RADIOELEKTRONICKE INZENYRSTVI, 2014, roč.~23, č.~1, s.~274-281. ISSN~1210-2512.
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