Fig 1.
A general block diagram of the proposed schizophrenia detection schemes.
The single-feature-type schemes follow the red arrows. The feature-fusion schemes are indicated by the blue arrows. For the decision-level fusion schemes, the orange arrows trace the path from the single-feature-type schemes to the final fused decisions.
Table 1.
Summary of participant demographics in the COBRE schizophrenia dataset.
Table 2.
Classification accuracy (%) with feature selection algorithms and feature types.
Table 3.
Schizophrenia detection results with single-feature-type classifiers and decision-level classifiers.
Feature selection was applied to each of the four single feature types (ALFF, fALFF, ReHo and VMHC). The decision-level classifier used all feature types except for the ALFF one, which has the worst performance. For comparison, the last column shows the corresponding accuracies obtained by Chyzhyk et al. [57].
Fig 2.
Average accuracy calculation for the decision-level fusion scheme.
Table 4.
Schizophrenia detection results with feature-level fusion of different pairwise, triple, and quadruple combinations of single feature types.
Table 5.
P-values for test of significance of the outcomes of the classifiers based on single-feature-type classifiers and decision-level classifier.
Statistical comparison is made with respect to the ReHo-based classifier.
Table 6.
P-values for test of significance of the outcomes of the classifiers based on feature-level fusion of different pairwise, triple, and quadruple combinations of single feature types.
Statistical comparison is made with respect to the ReHo-based classifier.
Fig 3.
ROC curves for schizophrenia classification using different feature combinations and ROC vertical averaging where the numbers in brackets denote the area under the curve (AUC): (a) Single feature types. (b) Pairwise feature type combinations. (c) Triple and quadruple combinations of features.
Fig 4.
Localization of the areas in the AAL atlas.
Circles represent AAL nodes. The blue circles represent the areas with higher discriminability between healthy and schizophrenia groups and the green circles represent unaffected areas.
Table 7.
The most discriminative measures and corresponding AAL regions.
Fig 5.
Schizophrenia detection performance outcomes under the Rician noise conditions for the best classifiers with single, pairwise, triple, and quadruple feature combinations as well as the decision-fusion classifier.
Table 8.
Effects of Rician noise on schizophrenia detection performance with single-feature-type and decision-level fusion schemes for fMRI data contaminated with Rician noise levels of σ = 1 and σ = 2.
Table 9.
Effects of Rician noise on schizophrenia detection performance with feature-level fusion of different pairwise, triple, and quadruple combinations of single feature types for fMRI data contaminated with Rician noise levels of σ = 1 and σ = 2.
Fig 6.
Contribution percentage of each of the ALFF, fALFF, ReHo, VMHC feature types in the performance of the classifiers with pairwise, triple, and quadruple feature combinations.