Autism Spectrum Disorder Detection in Children Via Deep Learning Models Based on Facial Images

Bawer Khan, Sohail Masood Bhatti and Arslan Akram
Additional contact information
Bawer Khan: Faculty of Computing, Department of Computer Science and Information Technology, The Superior University, Lahore, 54000, Pakistan
Sohail Masood Bhatti: Faculty of Computing, Department of Computer Science and Information Technology, The Superior University, Lahore, 54000, Pakistan
Arslan Akram: Faculty of Computing, Department of Computer Science and Information Technology, The Superior University, Lahore, 54000, Pakistan

Bulletin of Business and Economics (BBE), 2024, vol. 13, issue 1, 557-572

Abstract: Autism spectrum disorder (ASD) is a complicated psychiatric disease that causes difficulty in communicating with others, and restricted behavior, speech, as well as nonverbal interaction. Children with autism have unique facial characteristics that distinguish them from ordinarily developing children. Therefore, there is a requirement for a precise and automated system capable of early detection of autism in children, yielding accurate results. The objective of this research is to assist both families and psychiatrists in diagnosing autism through a straightforward approach. Specifically, the study employs a deep learning method that utilizes experimentally validated facial features. The technique involves a convolutional neural network along with transfer learning for the detection of autism. MobileNetv2, Xception, ResNet-50, VGG16 and DenseNet-121 were the pretrained models used for detection of autism. The evaluation of these models utilized a dataset sourced from Kaggle, comprising 2,940 facial images. We evaluated the five deep learning models using standard measures like recall, precision, accuracy, F1 score, and ROC curve. The proposed DenseNet-121 model outperformed existing transfer learning models, with 96% accuracy rate. With respect to performance evaluation, the proposed method exhibited superiority over the most recent models. Our model possesses the capability to support healthcare professionals in validating the precision of their initial screening for Autism Spectrum Disorders (ASDs) in pediatric patients.

Keywords: Autism spectrum disorder; Facial features; Convolutional neural network; Transfer learning; DenseNet-121 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://bbejournal.com/BBE/article/view/731/689 (application/pdf)
https://bbejournal.com/BBE/article/view/731 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rfh:bbejor:v:13:y:2024:i:1:p:557-572

DOI: 10.61506/01.00241

Access Statistics for this article

Bulletin of Business and Economics (BBE) is currently edited by Dr. Muhammad Irfan Chani

More articles in Bulletin of Business and Economics (BBE) from Research Foundation for Humanity (RFH) Contact information at EDIRC.
Bibliographic data for series maintained by Dr. Muhammad Irfan Chani ().