mlsanityData to 7 Oct 2026

ASD-DiagNet: A hybrid learning approach for detection of Autism Spectrum Disorder using fMRI data

arXiv 1904.07577 ↗First posted 16 Apr 2019cs.LG eess.IV stat.MLcited by 27

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Abstract (arXiv)

Mental disorders such as Autism Spectrum Disorders (ASD) are heterogeneous disorders that are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behavioural observation of symptomology (DSM-5/ICD-10) and may be prone to over-prescribing of drugs due to misdiagnosis. In order to move the field towards more quantitative fashion, we need advanced and scalable machine learning infrastructure that will allow us to identify reliable biomarkers of mental health disorders. In this paper, we propose a framework called ASD-DiagNet for classifying subjects with ASD from healthy subjects by using only fMRI data. We designed and implemented a joint learning procedure using an autoencoder and a single layer perceptron which results in improved quality of extracted features and optimized parameters for the model. Further, we designed and implemented a data augmentation strategy, based on linear interpolation on available feature vectors, that allows us to produce synthetic datasets needed for training of machine learning models. The proposed approach is evaluated on a public dataset provided by Autism Brain Imaging Data Exchange including 1035 subjects coming from 17 different brain imaging centers. Our machine learning model outperforms other state of the art methods from 13 imaging centers with increase in classification accuracy up to 20% with maximum accuracy of 80%. The machine learning technique presented in this paper, in addition to yielding better quality, gives enormous advantages in terms of execution time (40 minutes vs. 6 hours on other methods). The implemented code is available as GPL license on GitHub portal of our lab (https://github.com/pcdslab/ASD-DiagNet).

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Apr 2019.

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Citing papers per year: 2021: 2; 2022: 5; 2023: 6; 2024: 4; 2025: 6; 2026: 4.

27 papers.

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  1. 2023NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomicscited by 33
  2. 2022Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A reviewcited by 8
  3. 2023EAG-RS: A Novel Explainability-guided ROI-Selection Framework for ASD Diagnosis via Inter-regional Relation Learningcited by 5
  4. 2022Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regressioncited by 4
  5. 2021Identifying Autism Spectrum Disorder Based on Individual-Aware Down-Sampling and Multi-Modal Learningcited by 3
  6. 2023A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identificationcited by 3
  7. 2024Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processingcited by 3
  8. 2024MADE-for-ASD: A Multi-Atlas Deep Ensemble Network for Diagnosing Autism Spectrum Disordercited by 3
  9. 2024BrainMAP: Learning Multiple Activation Pathways in Brain Networkscited by 2
  10. 2021Functional Magnetic Resonance Imaging data augmentation through conditional ICAcited by 1
  11. 2022Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencodercited by 1
  12. 2023HDGL: A hierarchical dynamic graph representation learning model for brain disorder classificationcited by 1
15 more
  1. 2024HyperGALE: ASD Classification via Hypergraph Gated Attention with Learnable Hyperedgescited by 1
  2. 2025Efficient 4D fMRI ASD Classification using Spatial-Temporal-Omics-based Learning Frameworkcited by 1
  3. 2025Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networkscited by 1
  4. 2022Improving the Level of Autism Discrimination through GraphRNN Link Predictioncited by 0
  5. 2022Multimodal Brain Disease Classification with Functional Interaction Learning from Single fMRI Volumecited by 0
  6. 2023Comparison of Probabilistic Deep Learning Methods for Autism Detectioncited by 0
  7. 2023Early Autism Diagnosis based on Path Signature and Siamese Unsupervised Feature Compressorcited by 0
  8. 2025Brain Network Classification Based on Graph Contrastive Learning and Graph Transformercited by 0
  9. 2025A Confounding Factors-Inhibition Adversarial Learning Framework for Multi-site fMRI Mental Disorder Identificationcited by 0
  10. 2025Comorbidity-Informed Transfer Learning for Neuro-developmental Disorder Diagnosiscited by 0
  11. 2025Spectral Graph Neural Networks for Cognitive Task Classification in fMRI Connectomescited by 0
  12. 2026BrainSTR: Spatio-Temporal Contrastive Learning for Interpretable Dynamic Brain Network Modelingcited by 0
  13. 2026Multimodal Connectome Fusion via Cross-Attention for Autism Spectrum Disorder Classification Using Graph Learningcited by 0
  14. 2026BrainSCL: Subtype-Guided Contrastive Learning for Brain Disorder Diagnosiscited by 0
  15. 2026Graph Attention Network-Based Detection of Autism Spectrum Disordercited by 0