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Empirical Study of Autism Spectrum Disorder Diagnosis Using Facial Images by Improved Transfer Learning Approach.

Md Shafiul Alam¹, Muhammad Mahbubur Rashid¹, Rupal Roy¹

¹Department of Mechatronics Engineering, International Islamic University Malaysia, Kula Lumpur 43200, Malaysia.

Bioengineering (Basel, Switzerland)

|November 24, 2022

Summary

This study introduces a novel method using deep convolutional neural networks (CNNs) to detect autism spectrum disorder (ASD) in children via facial images. A modified Xception model achieved 95% accuracy, offering a promising tool for early ASD screening.

Keywords:

ASD diagnosis convolutional neural network (CNN)deep learning facial image transfer learning

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

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Published on: July 31, 2016

Area of Science:

Neurology
Computer Science
Biomedical Engineering

Background:

Autism spectrum disorder (ASD) presents significant challenges in diagnosis due to the absence of specific medical tests.
Early intervention is crucial for improving brain functionality in children with ASD.
Facial features can serve as potential biomarkers reflecting neurological development, aiding in early detection.

Purpose of the Study:

To investigate the efficacy of deep convolutional neural network (CNN)-based transfer learning approaches for detecting ASD in children using facial images.
To optimize CNN models by selecting the best hyperparameters and optimizers for enhanced diagnostic accuracy.
To develop a computational tool to assist clinicians in the early screening and validation of ASD diagnoses.

Main Methods:

Utilized several deep convolutional neural network (CNN) architectures, including Xception, VGG19, ResNet50V2, MobileNetV2, and EfficientNetB0, for facial image analysis.
Employed transfer learning techniques to adapt pre-trained models for the specific task of ASD detection.
Conducted an empirical study to fine-tune optimizers and hyperparameters for optimal model performance.

Main Results:

The modified Xception model achieved the highest accuracy of 95% in detecting ASD from facial images.
Other evaluated models showed strong performance: ResNet50V2 (94%), MobileNetV2 (92%), VGG19 (86.5%), and EfficientNetB0 (85.8%).
The developed transfer learning approaches demonstrated superior performance compared to existing methods.

Conclusions:

Deep learning models, particularly the modified Xception network, show significant potential for accurate and early detection of ASD using facial biomarkers.
This AI-driven approach can serve as a valuable supplementary tool for healthcare professionals in validating initial ASD screenings.
Facial image analysis via CNNs offers a non-invasive and accessible method for improving early diagnosis and intervention for autism spectrum disorder.