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• W4281392886 endingPage "e0265300" @default.
• W4281392886 startingPage "e0265300" @default.
• W4281392886 abstract "Mental disorders, especially schizophrenia, still pose a great challenge for diagnosis in early stages. Recently, computer-aided diagnosis techniques based on resting-state functional magnetic resonance imaging (Rs-fMRI) have been developed to tackle this challenge. In this work, we investigate different decision-level and feature-level fusion schemes for discriminating between schizophrenic and normal subjects. Four types of fMRI features are investigated, namely the regional homogeneity, voxel-mirrored homotopic connectivity, fractional amplitude of low-frequency fluctuations and amplitude of low-frequency fluctuations. Data denoising and preprocessing were first applied, followed by the feature extraction module. Four different feature selection algorithms were applied, and the best discriminative features were selected using the algorithm of feature selection via concave minimization (FSV). Support vector machine classifiers were trained and tested on the COBRE dataset formed of 70 schizophrenic subjects and 70 healthy subjects. The decision-level fusion method outperformed the single-feature-type approaches and achieved a 97.85% accuracy, a 98.33% sensitivity, a 96.83% specificity. Moreover, feature-fusion scheme resulted in a 98.57% accuracy, a 99.71% sensitivity, a 97.66% specificity, and an area under the ROC curve of 0.9984. In general, decision-level and feature-level fusion schemes boosted the performance of schizophrenia detectors based on fMRI features." @default.
• W4281392886 created "2022-05-25" @default.
• W4281392886 creator A5016458190 @default.
• W4281392886 creator A5028367824 @default.
• W4281392886 creator A5055895305 @default.
• W4281392886 creator A5090009857 @default.
• W4281392886 date "2022-05-24" @default.
• W4281392886 modified "2023-09-25" @default.
• W4281392886 title "Feature and decision-level fusion for schizophrenia detection based on resting-state fMRI data" @default.
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• W4281392886 doi "https://doi.org/10.1371/journal.pone.0265300" @default.
• W4281392886 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35609033" @default.

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