Deep Bridge: A Unified Convolutional Neural Network Framework for Dual-Task Healthcare Applications in Autism Spectrum Disorder Screening and Sign Language Recognition

Abstract

In this paper, we propose Deep Bridge, a unified Convolutional Neural Network (CNN) model that can be considered as a dual task model for healthcare, to solve two important problems: Autism Spectrum Disorder ( ASD ) screening from facial image analysis and recognizing American Sign Language ( ASL ) for better accessibility. It is a hierarchical Multi-Layer Perceptron (MLP) with 512-256-128-64 neurons in each layer, Rectified Linear Unit (ReLU) as an activation function, and Adam optimizer with early stopping regularization. Sixteen 64x64 RGB images were flattened to 12,288-dimensional feature vectors which are required for expression recognition, eye contact pattern and hand gestures essential for classification. Experimental evaluation on synthetic benchmark and clinical data demonstrates strong performance: 94.50% and 96.25% accuracy on autism screening and sign language recognition respectively, with weighted F1-scores of 0.945 and 0.962. In binary classification of autism, the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) is 0.967, which indicates high discriminative power. Statistical robustness is validated through 5-fold cross-validation yielding mean accuracies of 93.82% (+/-1.24%) and 95.68% (+/-0.87%). A web-based Streamlit interface enables real-time screening, with an average of 23.4ms for inference, making it a clinically deployable solution that connects research and clinical application of machine learning.