Autism Spectrum Disorder Detection Using Facial Images and Deep Convolutional Neural Networks
Lalitha Kumari Gaddala, Koteswara Rao Kodepogu, Yalamanchili Surekha, M. Tejaswi, Kethineni Ameesha, Lakshman Saketh Kollapalli, Sriram Kotha, Vijaya Bharathi Manjeti
Abstract
Autism Spectrum Disorder (ASD) is a prevalent neurodevelopmental disorder, affecting approximately 1% of the global population.It is characterised by deficits in social communication, interaction, and a propensity for repetitive behaviours.Despite its prevalence, the diagnosis of ASD remains challenging due to the lack of conspicuous disparities between neuroimages of affected individuals and their neurotypical counterparts.This study aims to enhance the accuracy and efficiency of ASD diagnosis by integrating deep learning techniques with conventional diagnostic procedures.In this work, we present a novel approach to detect and classify ASD using facial images processed through deep Convolutional Neural Networks (CNNs).We utilised the Visual Geometry Group models (VGG16 and VGG19) to construct our deep learning models.The models were trained and validated using an extensive dataset of facial images.The proposed models have demonstrated promising results, achieving an accuracy rate of 84% in the classification of ASD individuals.This study's findings suggest the potential of deep learning applications in refining the diagnostic process of Autism Spectrum Disorder.Further research is recommended to optimise these models and validate their effectiveness on a broader scale.