Sadek, Esraa and AbdElSabour Seada, Noha and Ghoniemy, Said (2020) Computer Vision Techniques for Autism Symptoms Detection and Recognition: A Survey. International Journal of Intelligent Computing and Information Sciences, 20 (2). pp. 89-111. ISSN 2535-1710
IJICIS_Volume 20_Issue 2_Pages 89-111.pdf - Published Version
Download (725kB)
Abstract
Autism spectrum disorder (ASD) is a world-threatening mental developing disorders that recently appeared widely, due to its diagnosis complexity as well as lack of evidence of its real causes. Many researchers have afforded great effort to precisely identify this syndrome and its symptoms. This survey provides a comprehensive study of autism spectrum disorder, its types, symptoms, prevalence, and developments in its diagnosing. Six categories for autism exposure and identification are currently investigated; clinical monitoring, genetics and blood analysis, Functional magnetic resonance imaging (fMRI), Electroencephalography (EEG) based investigation, wearable sensors and finally computer vision-based techniques. Computational technologies, especially computer-vision, machine learning and neural networks techniques have added great advances in detecting autism and these techniques are comprehensively reviewed in this paper. Also, medical assisting computer vision-based framework is proposed to detect observable autism symptoms. The proposed framework utilises recent and efficient techniques that can be used to produce accurate diagnosing results.
Item Type: | Article |
---|---|
Subjects: | Middle Asian Archive > Computer Science |
Depositing User: | Managing Editor |
Date Deposited: | 29 Jun 2023 05:14 |
Last Modified: | 25 May 2024 09:30 |
URI: | http://library.eprintglobalarchived.com/id/eprint/903 |