Efficient deep learning-based data-centric approach for autism spectrum disorder diagnosis from facial images using explainable AI
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-centr...
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my.iium.irep.1065262024-02-09T08:46:09Z http://irep.iium.edu.my/106526/ Efficient deep learning-based data-centric approach for autism spectrum disorder diagnosis from facial images using explainable AI 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 TA1501 Applied optics. Lasers TA329 Engineering mathematics. Engineering analysis 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. MDPI 2023-08-29 Article PeerReviewed application/pdf en http://irep.iium.edu.my/106526/7/106526_Efficient%20deep%20learning-based%20data-centric%20approach.pdf application/pdf en http://irep.iium.edu.my/106526/13/106526_%20Efficient%20deep%20learning-based%20data-centric%20approach_Scopus.pdf Alam, Mohammad Shafiul and Rashid, Muhammad Mahbubur and Faizabadi, Ahmed Rimaz and Mohd Zaki, Hasan Firdaus and Alam, Tasfiq E. and Ali, Md Shahin and Gupta, Kishor Datta and Ahsan, Md Manjurul (2023) Efficient deep learning-based data-centric approach for autism spectrum disorder diagnosis from facial images using explainable AI. Technologies, 11 (5). pp. 1-27. E-ISSN 2227-7080 https://www.mdpi.com/2227-7080/11/5/115 https://doi.org/10.3390/technologies11050115 |
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TA1501 Applied optics. Lasers TA329 Engineering mathematics. Engineering analysis 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 Efficient deep learning-based data-centric approach for autism spectrum disorder diagnosis from facial images using explainable AI |
description |
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. |
format |
Article |
author |
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 |
author_facet |
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 |
author_sort |
Alam, Mohammad Shafiul |
title |
Efficient deep learning-based data-centric approach for autism spectrum disorder diagnosis from facial images using explainable AI |
title_short |
Efficient deep learning-based data-centric approach for autism spectrum disorder diagnosis from facial images using explainable AI |
title_full |
Efficient deep learning-based data-centric approach for autism spectrum disorder diagnosis from facial images using explainable AI |
title_fullStr |
Efficient deep learning-based data-centric approach for autism spectrum disorder diagnosis from facial images using explainable AI |
title_full_unstemmed |
Efficient deep learning-based data-centric approach for autism spectrum disorder diagnosis from facial images using explainable AI |
title_sort |
efficient deep learning-based data-centric approach for autism spectrum disorder diagnosis from facial images using explainable ai |
publisher |
MDPI |
publishDate |
2023 |
url |
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 |
_version_ |
1792146404188094464 |
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13.18916 |