ArticlePloS one2022
Feature and decision-level fusion for schizophrenia detection based on resting-state fMRI data.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 3 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence approaches for schizophrenia prediction and its biomarkers using medical imaging data.Frontiers in psychiatry · 2026Pooled it
- The Intersection of Genetics and Neuroimaging: A Systematic Review of Imaging Genetics in Neurological Disease for Personalized Treatment.Journal of molecular neuroscience : MN · 2025Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Investigating Resting-State Brain Activity in Adolescents with High-Functioning Autism Spectrum Disorder: Linking Fractional Amplitude of Low-Frequency Fluctuation to Clinical Symptoms.Journal of autism and developmental disorders · 2026Article
- Optimizing Renal Fibrosis Assessment in Patients With Chronic Kidney Disease: Feature and Decision-Level Fusion in Machine Learning Using Clinical and Ultrasound Information.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
- A novel strategy for enhanced schizophrenia detection using established CNN architectures.BMC medical imaging · 2025Article
- Confound Controlled Multimodal Neuroimaging Data Fusion and Its Application to Developmental Disorders.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2025Article
- Distinct connectivity patterns between perception and attention-related brain networks characterize dyslexia: Machine learning applied to resting-state fMRI.Cortex; a journal devoted to the study of the nervous system and behavior · 2024Article
- Identification of male schizophrenia patients using brain morphology based on machine learning algorithms.World journal of psychiatry · 2024Article
- Multi-modal MRI for objective diagnosis and outcome prediction in depression.NeuroImage. Clinical · 2024Article
- MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging.PloS one · 2024Article
- Resting-state functional MRI in treatment-resistant schizophrenia.Frontiers in neuroimaging · 2023Review
Corrections and comments
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Authors and funding
4 authors.
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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.
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