A Deep Learning Framework for Building Social Connections in Individuals with Autism
DOI:
https://doi.org/10.52783/jns.v14.3651Keywords:
Deep Learning, Transfer Learning, Social Network, Recommender System, Autism Spectrum Disorder (ASD)Abstract
Biomedical images and social media data can be incorporated to detect Autism Spectrum Disorder (ASD), a type of neurological or brain-related problem. A neurological condition called autism spectrum disorder (ASD) is linked with brain progress and consequently affects how the face looks on the outside. ASD children differ significantly from normal children called typically developed (TD) children in that they have different facial landmarks. The proposed research is novel that aims to create a system based on facial recognition and social media for autism spectrum disorder detection. Deep learning techniques are used to identify these landmarks, but they need precise technology to extract and create the right patterns of the facial features. This study uses a deep learning algorithm that is, a convolutional neural network (CNN) with transfer learning to facilitate communities and psychiatrists experimentally detect autism based on facial features. Pre-trained models such as EfficientNet, Xception, Visual Geometry Group Network (VGG19) and NASNETMobile were applied to the classification task. Performance assessment standards such as accuracy, specificity, and sensitivity were used to compare the outcomes of these models. With the accuracy result of 92.33%, VGG19 model outperformed EfficientNet (78.57%), NASNETMobile (62.24%) and Xception (78.91%) in autism detection in patients
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