Effect of Different Modalities of Facial Images on ASD Diagnosis using Deep Learning-Based Neural Network

Authors

  • Mohammad Shafiul Alam Department of Electrical and Electronic Engineering, Faculty of Science and Engineering, Northern University Bangladesh, Dhaka, Bangladesh
  • Zabina Tasneem Department of Mechatronics Engineering, Kulliyah of Engineering, International Islamic University Malaysia, 53100 Jln Gombak, Malaysia
  • Sher Afghan Khan Department of Mechatronics Engineering, Kulliyah of Engineering, International Islamic University Malaysia, 53100 Jln Gombak, Malaysia
  • Muhammad Mahbubur Rashid Department of Mechatronics Engineering, Kulliyah of Engineering, International Islamic University Malaysia, 53100 Jln Gombak, Malaysia

DOI:

https://doi.org/10.37934/araset.32.3.5974

Keywords:

Deep Learning, depth image, facial image, Autism Spectrum Disorder, explainable AI

Abstract

This paper aims to investigate the effectiveness of different modalities of facial images for diagnosing Autism Spectrum Disorder (ASD) using deep learning-based neural networks. The motivation behind this study is the potential of advanced technologies to aid in accurately diagnosing ASD. The research revolves around the need to explore the performance of deep learning models on different modalities of facial images and to identify the challenges and potential solutions associated with each modality. The methodology involves training and testing the models on the respective datasets and analysing their accuracy and performance. ResNet50V2 achieved a 100% accuracy on the 2D test dataset, while Xception achieved an accuracy of 93.75% on the 3D test set. The detection accuracy suggests that neural networks-based deep learning methods have the potential to diagnose ASD using facial images accurately. However, the models perform better on 2D data, highlighting the need for additional training on larger 3D datasets to improve accuracy on 3D images. The study contributes to the field by providing insights into the performance of different modalities of facial images, emphasizing the need for robust datasets, and suggesting future research directions to enhance the accuracy and efficiency of ASD diagnosis using deep learning techniques.

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Author Biographies

Mohammad Shafiul Alam, Department of Electrical and Electronic Engineering, Faculty of Science and Engineering, Northern University Bangladesh, Dhaka, Bangladesh

alam.s@live.iium.edu.my

Zabina Tasneem , Department of Mechatronics Engineering, Kulliyah of Engineering, International Islamic University Malaysia, 53100 Jln Gombak, Malaysia

zabinatasneem@gmail.com

Sher Afghan Khan, Department of Mechatronics Engineering, Kulliyah of Engineering, International Islamic University Malaysia, 53100 Jln Gombak, Malaysia

sakhan@iium.edu.my

Muhammad Mahbubur Rashid , Department of Mechatronics Engineering, Kulliyah of Engineering, International Islamic University Malaysia, 53100 Jln Gombak, Malaysia

mahbub@iium.edu.my

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Published

2023-10-04

How to Cite

Alam, M. S., Zabina Tasneem, Khan, S. A. ., & Rashid , M. M. . (2023). Effect of Different Modalities of Facial Images on ASD Diagnosis using Deep Learning-Based Neural Network. Journal of Advanced Research in Applied Sciences and Engineering Technology, 32(3), 59–74. https://doi.org/10.37934/araset.32.3.5974

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Section

Articles