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Updated: Sep 16, 2025

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

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Published on: July 31, 2016

Robust Autism Spectrum Disorder Screening Based on Facial Images (For Disability Diagnosis): A Domain-Adaptive Deep

Mohammad Shafiul Alam^1,2, Muhammad Mahbubur Rashid¹, Ahmad Jazlan¹

¹Department of Mechatronics Engineering, International Islamic University Malaysia, Kuala Lumpur 50728, Malaysia.

Diagnostics (Basel, Switzerland)

|July 12, 2025

Summary

This summary is machine-generated.

A new deep ensemble learning system, ASD-UANet, accurately classifies autism spectrum disorder (ASD) using facial images. This AI approach shows high accuracy and generalizability, offering a promising tool for early ASD detection.

Keywords:

autism spectrum disorder deep learning domain adaptation ensemble learning facial image dataset

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Area of Science:

Artificial intelligence in healthcare
Deep learning for medical diagnosis
Disability inclusion technologies

Background:

Artificial intelligence (AI) is transforming healthcare for individuals with disabilities, including those with autism spectrum disorder (ASD).
Inconsistent data from diverse sources presents a significant challenge in developing reliable AI diagnostic tools for ASD.
Accurate and dependable classification of ASD using facial images requires robust deep learning methodologies.

Purpose of the Study:

To develop and evaluate a deep ensemble learning system for accurate ASD classification from facial images.
To address data inconsistencies by integrating multiple public datasets.
To assess the generalizability of the developed system on unseen, real-time data.

Main Methods:

Utilized two public ASD facial image datasets (Kaggle and YTUIA) with varying demographics and image characteristics.
Developed the ASD-UANet ensemble model by combining Xception and ResNet50V2 architectures using a weighted ensemble strategy (FPPR).
Evaluated model performance on combined datasets stratified by age and gender, and tested generalizability on an unseen real-time dataset (UIFID).

Main Results:

The ASD-UANet ensemble achieved 96.0% accuracy and an AUC of 0.990 on the combined dataset (T1+T2), outperforming individual models.
Demonstrated strong generalizability on the unseen real-time dataset (T3) with 90.6% accuracy and an AUC of 0.930.
Significantly outperformed individual transfer learning models, such as Xception alone (83% accuracy on T1+T2).

Conclusions:

The developed ASD-UANet system shows significant potential for equitable and clinically beneficial ASD screening.
This non-invasive, cost-effective method offers a foundation for more precise diagnoses and improved inclusion for individuals with ASD.
Integrating diverse data sources and ensemble deep learning models enhances diagnostic accuracy and reliability for ASD.