Classification of First-Episode Schizophrenia Using Wavelet Imaging Features
Language English Country Netherlands Media print
Document type Journal Article
PubMed
32570589
DOI
10.3233/shti200372
PII: SHTI200372
Knihovny.cz E-resources
- Keywords
- Machine learning, neuroimaging, schizophrenia, support vector machines,
- MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Schizophrenia * MeSH
- Support Vector Machine MeSH
- Wavelet Analysis MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
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.
Brno University of Technology Technická 3058 10 61600 Brno Czech Republic
Institute of Biostatistics and Analyses Ltd Czech Republic
Masaryk University Faculty of Medicine Kamenice 5 62500 Brno Czech Republic
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