A deep learning-based ensemble for autism spectrum disorder diagnosis using facial images.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder leading to an inability to socially communicate and in extreme cases individuals are completely dependent on caregivers. ASD detection at early ages is crucial as early detection can reduce the effect on social impairment. Deep learning models have shown capability to detect ASD earlier compared to traditional detection methods used by clinics and experts. Ensemble models, renowned for their ability to enhance predictive performance by combining multiple models, have emerged as a powerful tool in machine learning. This study harnesses the strength of ensemble learning to address the critical challenge of ASD diagnosis. This study proposed a deep ensemble model leveraging the strengths of VGG16 and Xception net trained on Facial Images for ASD detection overcoming limitations in existing datasets through extensive preprocessing. Proposed model preprocessed the training dataset of facial images by converting side posed images into frontal face images, using Histogram Equalization (HE) to enhance colors, data augmentation techniques application, and using the Hue Saturation Value (HSV) color model. By integrating the feature extraction strengths of VGG16 and Xception with fully connected layers, our model has achieved a notable 97% accuracy on the Kaggle ASD Face Image Dataset. This approach supports early detection of ASD and aligns with Sustainable Development Goal 3, which focuses on improving health and well-being.