Diagnosis of Autism in Children Using Deep Learning Techniques by Analyzing Facial Features

Abstract

Autism spectrum disorder (ASD) is a complex neurological disorder that results in aberrant personality traits, cognitive function, and interpersonal relationships. It impacts the child’s linguistic and social skills, interaction abilities, and capacity for logical thought. It is possible to use the human face as a physiological identifier since it can serve as an indicator of brain function, thus helping with early diagnosis in a simple and effective way. The purpose of this study is to detect autism from facial images using a deep learning model. To accurately identify autism in children, we used three pre-trained CNN models, VGG16, VGG19 and, EfficientnetB0, as feature extractors and binary classifiers. The suggested models were trained using a publicly available dataset from Kaggle that included 3014 images of children characterized as autistic and non-autistic. The models yielded accuracies of 84.66%, 80.05%, and 87.9%, respectively.