Efficient deep learning-based data-centric approach for autism spectrum disorder diagnosis from facial images using explainable AI

• Main Authors: Alam, Mohammad Shafiul, Rashid, Muhammad Mahbubur, Faizabadi, Ahmed Rimaz, Mohd Zaki, Hasan Firdaus, Alam, Tasfiq E., Ali, Md Shahin, Gupta, Kishor Datta, Ahsan, Md Manjurul
• Format: Article
• Language: English; English
• Published: MDPI 2023
• Subjects: TA1501 Applied optics. Lasers; TA329 Engineering mathematics. Engineering analysis
• Online Access: http://irep.iium.edu.my/106526/7/106526_Efficient%20deep%20learning-based%20data-centric%20approach.pdf; http://irep.iium.edu.my/106526/13/106526_%20Efficient%20deep%20learning-based%20data-centric%20approach_Scopus.pdf; http://irep.iium.edu.my/106526/; https://www.mdpi.com/2227-7080/11/5/115; https://doi.org/10.3390/technologies11050115

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.