# Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review

arXiv: https://arxiv.org/abs/2007.01285 · HTML: https://mlsanity.com/paper/2007.01285/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2020.

## Built on by (most cited first, 26 of 26)

- [Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data](https://mlsanity.com/paper/2102.06388/) (2021)
- [An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2103.03081/) (2021)
- [Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models](https://mlsanity.com/paper/2109.01120/) (2021)
- [A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism](https://mlsanity.com/paper/2303.03577/) (2023)
- [Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images](https://mlsanity.com/paper/2102.06883/) (2021)
- [Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review](https://mlsanity.com/paper/2105.04881/) (2021)
- [Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies](https://mlsanity.com/paper/2109.04364/) (2021)
- [Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review](https://mlsanity.com/paper/2206.11233/) (2022)
- [Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning](https://mlsanity.com/paper/2104.11949/) (2021)
- [Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patients](https://mlsanity.com/paper/2104.08954/) (2021)
- [Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression](https://mlsanity.com/paper/2205.15858/) (2022)
- [ViTASD: Robust Vision Transformer Baselines for Autism Spectrum Disorder Facial Diagnosis](https://mlsanity.com/paper/2210.16943/) (2022)
- [Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research](https://mlsanity.com/paper/2301.08525/) (2023)
- [An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2105.14278/) (2021)
- [A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification](https://mlsanity.com/paper/2302.07243/) (2023)
- [MADE-for-ASD: A Multi-Atlas Deep Ensemble Network for Diagnosing Autism Spectrum Disorder](https://mlsanity.com/paper/2407.07076/) (2024)
- [Evaluation of augmentation methods in classifying autism spectrum disorders from fMRI data with 3D convolutional neural networks](https://mlsanity.com/paper/2110.10489/) (2021)
- [Deep Integrated Pipeline of Segmentation Guided Classification of Breast Cancer from Ultrasound Images](https://mlsanity.com/paper/2110.14013/) (2021)
- [Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regression](https://mlsanity.com/paper/2109.05461/) (2021)
- [Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder](https://mlsanity.com/paper/2204.03654/) (2022)
- [Meta-data Study in Autism Spectrum Disorder Classification Based on Structural MRI](https://mlsanity.com/paper/2206.05052/) (2022)
- [What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors](https://mlsanity.com/paper/2109.05457/) (2021)
- [OpenNDD: Open Set Recognition for Neurodevelopmental Disorders Detection](https://mlsanity.com/paper/2306.16045/) (2023)
- [Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review](https://mlsanity.com/paper/2407.04712/) (2024)
- [Explainable AI for Autism Diagnosis: Identifying Critical Brain Regions Using fMRI Data](https://mlsanity.com/paper/2409.15374/) (2024)
- [Evaluating the Effect of Frame Rate in Sequence-Based Classification of Autism-Related Self-Stimulatory Hand Idiosyncrasies](https://mlsanity.com/paper/2607.07957/) (2026)

## Abstract (arXiv)

Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works.