Informace o publikaci

Classification of First-Episode Schizophrenia Using Wavelet Imaging Features

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MARŠÁLOVÁ Kateřina SCHWARZ Daniel PROVAZNIK Ivo

Rok publikování 2020
Druh Článek ve sborníku
Konference Digital Personalized Health and Medicine
Fakulta / Pracoviště MU

Lékařská fakulta

Citace
www https://ebooks.iospress.nl/volumearticle/54374
Doi http://dx.doi.org/10.3233/SHTI200372
Klíčová slova Machine learning; neuroimaging; schizophrenia; support vector machines
Popis 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.

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