Deep learning-based recognition of autism using facial datasets

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that impacts communication and social interaction, and early detection is vital for timely intervention. This research explores the efficacy of deep learning algorithms in diagnosing ASD using facial expression analysis. We employed several deep learning architectures, including VGG16, VGG19, Xception, and EfficientNetB2, to classify facial expressions in the Autism Image Data datasets sourced from the Kaggle website. Our comparative analysis revealed that the EfficientNetB2 model surpassed the other models in all evaluation metrics: Specificity, Sensitivity, and Accuracy. With a specificity of 90.00% and a sensitivity of 93.33%, EfficientNetB2 demonstrated superior accuracy in identifying ASD cases compared to its counterparts. These results underscore the potential of EfficientNetB2 in enhancing the early diagnostic process for ASD through precise facial expression recognition, suggesting that advanced deep learning networks can significantly aid in the diagnosis and subsequent interventions for individuals with ASD.