ArticleFrontiers in neuroinformatics2021
DarkASDNet: Classification of ASD on Functional MRI Using Deep Neural Network.
Article in Frontiers in neuroinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning approach to predict autism spectrum disorder: a systematic review and meta-analysis.BMC psychiatry · 2024Pooled it
- Sales-Training-Inspired Optimization for Deep High-Order Principal Network in Autism Spectrum Disorder Classification.International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience · 2026Article
- Diagnostic classification of children and adolescents with high-functioning autism spectrum disorder based on brain functional network characteristics of the dense individualized and common connectivity-based cortical landmark model.Frontiers in psychiatry · 2026Article
- Blurred magnitude homology of functional connectome for ASD diagnosis.Frontiers in psychiatry · 2025Article
- A short investigation of the effect of the selection of human brain atlases on the performance of ASD's classification models.Frontiers in neuroscience · 2025Review
- Advancements in automated diagnosis of autism spectrum disorder through deep learning and resting-state functional mri biomarkers: a systematic review.Cognitive neurodynamics · 2024Review
- Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder.Scientific reports · 2024Article
- Category-Selective Representation of Relationships in the Visual Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Multi-Slice Generation sMRI and fMRI for Autism Spectrum Disorder Diagnosis Using 3D-CNN and Vision Transformers.Brain sciences · 2023Article
- Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey.Biomedicines · 2023Article
- A Model for Diagnosing Autism Patients Using Spatial and Statistical Measures Using rs-fMRI and sMRI by Adopting Graphical Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Prediction Model for Sensory Perception Abnormality in Autism Spectrum Disorder.International journal of molecular sciences · 2023Article
- Systematic bibliometric and visualized analysis of research hotspots and trends in artificial intelligence in autism spectrum disorder.Frontiers in neuroinformatics · 2023Article
- Brain disease research based on functional magnetic resonance imaging data and machine learning: a review.Frontiers in neuroscience · 2023Review
- Feature Signature Discovery for Autism Detection: An Automated Machine Learning Based Feature Ranking Framework.Computational intelligence and neuroscience · 2023Article
- Construction of an immune-related ceRNA network to screen for potential diagnostic markers for autism spectrum disorder.Frontiers in genetics · 2022Article
- Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review.Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-invasive whole-brain scans aid the diagnosis of neuropsychiatric disorder diseases such as autism, dementia, and brain cancer. The assessable analysis for autism spectrum disorders (ASD) is rationally challenging due to the limitations of publicly available datasets. For diagnostic or prognostic tools, functional Magnetic Resonance Imaging (fMRI) exposed affirmation to the biomarkers in neuroimaging research because of fMRI pickup inherent connectivity between the brain and regions. There are profound studies in ASD with introducing machine learning or deep learning methods that have manifested advanced steps for ASD predictions based on fMRI data. However, utmost antecedent models have an inadequacy in their capacity to manipulate performance metrics such as accuracy, precision, recall, and F1-score. To overcome these problems, we proposed an avant-garde DarkASDNet, which has the competence to extract features from a lower level to a higher level and bring out promising results. In this work, we considered 3D fMRI data to predict binary classification between ASD and typical control (TC). Firstly, we pre-processed the 3D fMRI data by adopting proper slice time correction and normalization. Then, we introduced a novel DarkASDNet which surpassed the benchmark accuracy for the classification of ASD. Our model's outcomes unveil that our proposed method established state-of-the-art accuracy of 94.70% to classify ASD vs. TC in ABIDE-I, NYU dataset. Finally, we contemplated our model by performing evaluation metrics including precision, recall, F1-score, ROC curve, and AUC score, and legitimize by distinguishing with recent literature descriptions to vindicate our outcomes. The proposed DarkASDNet architecture provides a novel benchmark approach for ASD classification using fMRI processed data.
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