ArticleCognitive neurodynamics2023
Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression.
Article in Cognitive neurodynamics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- Gut-brain axis and neuroplasticity in health and disease: a systematic review.La Radiologia medica · 2025Pooled it
- A new framework for mental illnesses diagnosis using wearable devices aided by improved convolutional neural network.Scientific reports · 2025Article
- Exploiting adaptive neuro-fuzzy inference systems for cognitive patterns in multimodal brain signal analysis.Scientific reports · 2025Article
- Weaker top-down cognitive control and stronger bottom-up signaling transmission as a pathogenesis of schizophrenia.Schizophrenia (Heidelberg, Germany) · 2025Article
- Early attention-deficit/hyperactivity disorder (ADHD) with NeuroDCT-ICA and rhinofish optimization (RFO) algorithm based optimized ADHD-AttentionNet.Scientific reports · 2025Article
- A Pathological Diagnosis Method for Fever of Unknown Origin Based on Multipath Hierarchical Classification: Model Design and Validation.JMIR formative research · 2024Article
- Multimodality model investigating the impact of brain atlases, connectivity measures, and dimensionality reduction techniques on Attention Deficit Hyperactivity Disorder diagnosis using resting state functional connectivity.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Detection of Schizophrenia from EEG Signals using Selected Statistical Moments of MFC Coefficients and Ensemble Learning.Neuroinformatics · 2024Article
- Classification algorithm for motor imagery fusing CNN and attentional mechanisms based on functional near-infrared spectroscopy brain image.Cognitive neurodynamics · 2024Article
- A novel memristive neuron model and its energy characteristics.Cognitive neurodynamics · 2024Article
- Machine to brain: facial expression recognition using brain machine generative adversarial networks.Cognitive neurodynamics · 2024Article
- Using artificial intelligence methods to study the effectiveness of exercise in patients with ADHD.Frontiers in neuroscience · 2024Article
- Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review.Clinical practice and epidemiology in mental health : CP & EMH · 2024Review
- BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging.Bioengineering (Basel, Switzerland) · 2023Article
- An Approach to Binary Classification of Alzheimer's Disease Using LSTM.Bioengineering (Basel, Switzerland) · 2023Article
- Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey.Biomedicines · 2023Article
- Augmented Reality Surgical Navigation System Integrated with Deep Learning.Bioengineering (Basel, Switzerland) · 2023Article
- Review
- A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives.Diagnostics (Basel, Switzerland) · 2022Review
- Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review.Frontiers in molecular neuroscience · 2022Review
Corrections and comments
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Authors and funding
10 authors at 8 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.