Early Onset Autism Detection Using Deep Learning Techniques

Abstract

A neurological condition called autism spectrum disorder (ASD) that affects brain development can also affect how the face looks physically. The strategies that have been used to identify autistic input are reviewed in this study, along with a visual representation of each model's accuracy. In general, compared to typically developing (TD) youngsters, children with ASD have distinctive facial landmarks that make them stand out. The novel aspect of the research proposal is the development of a system based on face recognition and social media for the detection of autism spectrum disorders. Deep learning techniques may be used to recognize these landmarks, but they need a precise method of identifying and producing the right patterns of facial features. Face characteristics and a Convolutional Neural Network (CNN) were found to be efficient in this survey to detect Autism. According to the study's findings, the autism spectrum disorder can be recognized using facial recognition by automatic feature extraction and CNN classification.

Note:
Funding Information: The author(s) received no financial support for the research, authorship, and/or publication of this article.

Conflict of Interests: The authors have no conflicts of interest to declare. All co-authors have seen and agree with the contents of the manuscript and there is no financial interest to report.

Keywords: Autism, ASD, CNN, Deep Learning, Facial Features