DHCA‐Net: A Novel Dual‐Stream Hierarchical Channel Attention Network for Explainable Autism Spectrum Disorder Detection From Facial Images

Abstract

Autism spectrum disorder (ASD) consists of a spectrum of neurodevelopmental conditions characterized by complex behavioural traits and subtle, atypical facial morphologies. Analysing these facial biomarkers provides a promising, non‐invasive avenue for objective clinical screening, addressing the subjectivity of traditional diagnostic processes. Therefore, the present study aims to introduce DHCA‐Net, a deep learning framework designed for automated ASD detection through the analysis of facial images. The proposed architecture uses different levels of attention and different adaptive tuning to advance discriminative learning. Specifically, DHCA‐Net introduces three novel attention mechanisms to advance discriminative learning: a hierarchical channel module for spatial‐semantic processing, a temporal‐depth convolutional module for local contextual control and an inverted residual multi‐core module for dynamic feature learning. An adaptive refinement step is also introduced to denoise clinical features. To bolster and diversify the classifier, the model exploits deep spatial and contextual resources via a multi‐head feature fusion (MHFF) mechanism. We conducted extensive testing on a benchmark dataset comprising 2936 diverse, preprocessed facial images (86.4% train, 10.2% test, 3.4% validation splits). A comparative analysis evaluated DHCA‐Net against architectures such as DenseNet, Xception, EfficientNet and Swin‐Transformer. The proposed model achieved 93.7% classification accuracy, a 0.9365 F1 score and a 0.9887 AUC, demonstrating superior performance. Furthermore, the model maintains an efficient average inference latency of 70.14 ms despite its mid‐to‐high computational complexity. This explainable framework offers significant clinical applicability for scalable screening, though future work must address generalization across broader demographic populations.