Multiscale attention-based network to enhance detection and classification of autism spectrum disorders using convolutional neural network

Artificial intelligence (AI) and machine learning (ML) have made significant advances in the early detection and diagnosis of autism spectrum disorder (ASD), overcoming the limits of previous screening methods. These AI-based technologies offer more objective, scalable, and efficient methods for identifying risk behaviors associated with ASD. This article presents a novel approach for enhancing the detection and classification of ASD by integrating squeeze-and-excitation, multiscale attention mechanisms, and convolutional neural networks (CNNs) with automated hyperparameter optimization using the white shark optimization (WSO) algorithm. By leveraging attention mechanisms to focus on relevant facial features across multiple scales, this method enhances feature extraction, improves classification accuracy, and provides a robust framework for analyzing complex facial imaging data. An extensive autism dataset, encompassing both facial and multimodal datasets, was utilized in this study, including subjects from the non-ASD control (NC) group and individuals diagnosed with ASD. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art methods, achieving a high accuracy of 95.36%, precision of 92.62%, and an F1-score of 95.5% for ASD detection and classification. This proposed model is a promising tool for the accurate and early identification of ASD, which is crucial for effective treatment and management. By providing deeper insights into distinctive facial patterns and morphological features associated with ASD, the model enables physicians to make more informed decisions and develop targeted treatment plans, ultimately improving patient outcomes.