Classification of First-Episode Schizophrenia Using Wavelet Imaging Features
| Authors | |
|---|---|
| Year of publication | 2020 |
| Type | Article in Proceedings |
| Conference | Digital Personalized Health and Medicine |
| MU Faculty or unit | |
| Citation | |
| Web | https://ebooks.iospress.nl/volumearticle/54374 |
| Doi | http://dx.doi.org/10.3233/SHTI200372 |
| Keywords | Machine learning; neuroimaging; schizophrenia; support vector machines |
| Description | 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. |