A Siamese Network-Based Approach for Autism Spectrum Disorder Detection with Dual Architecture

Autism Spectrum Disorder (ASD) is a sophisticated neuro-developmental condition impacting numerous children. Early detection of ASD is crucial to implement suitable treatments to improve the daily activities of people with ASD. This paper introduces a system for ASD detection using facial images. The proposed model presents a unique system inspired by Siamese networks. Unlike traditional Siamese networks focusing on input pairs, our model leverages architectural pairs for feature combinations. During training, we combine features learned from different or the same architectures. This enables information transfer and improves the model’s capture of comprehensive patterns. Experimental results on the 2940 facial images dataset demonstrate the effectiveness of our system, which exhibits improved accuracy compared to using individual architectures. When (ResNet50, VGG16) architecture pairs are employed in the proposed approach, the highest performance is obtained with an accuracy of 78.57%. Leveraging the strengths of multiple architectures, our model provides a comprehensive and robust representation of input data, leading to improved performance.