Efficient Deep Learning-Based Data-Centric Approach for Autism Spectrum Disorder Diagnosis from Facial Images Using Explainable AI

Author:

Alam Mohammad Shafiul1,Rashid Muhammad Mahbubur1,Faizabadi Ahmed Rimaz1ORCID,Mohd Zaki Hasan Firdaus1,Alam Tasfiq E.2,Ali Md Shahin3ORCID,Gupta Kishor Datta4ORCID,Ahsan Md Manjurul2ORCID

Affiliation:

1. Department of Mechatronics Engineering, International Islamic University, Jln Gomak, Kuala Lumpur 53100, Malaysia

2. Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, USA

3. Department of Biomedical Engineering, Islamic University, Kushtia 7003, Bangladesh

4. Computer and Information Science, Clark Atlanta University, Atlanta, GA 30314, USA

Abstract

The research describes an effective deep learning-based, data-centric approach for diagnosing autism spectrum disorder from facial images. To classify ASD and non-ASD subjects, this method requires training a convolutional neural network using the facial image dataset. As a part of the data-centric approach, this research applies pre-processing and synthesizing of the training dataset. The trained model is subsequently evaluated on an independent test set in order to assess the performance matrices of various data-centric approaches. The results reveal that the proposed method that simultaneously applies the pre-processing and augmentation approach on the training dataset outperforms the recent works, achieving excellent 98.9% prediction accuracy, sensitivity, and specificity while having 99.9% AUC. This work enhances the clarity and comprehensibility of the algorithm by integrating explainable AI techniques, providing clinicians with valuable and interpretable insights into the decision-making process of the ASD diagnosis model.

Funder

International Islamic University Malaysia

Publisher

MDPI AG

Subject

Computer Science (miscellaneous)

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