Autism Spectrum Disorder Classification via Local and Global Feature Representation of Facial Image

Md Nadim Mahamood, Md Zasim Uddin, Md Arif Shahriar, Fady Alnajjar, Md Atiqur Rahman Ahad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects social communication and interaction. Early diagnosis of ASD can mitigate the severity and help with ideal treatment direction. Computer vision-based methods with traditional machine learning and deep learning are employed in the literature for automatic diagnosis. Recently, deep learning with a facial image-based ASD classification has gained interest due to its ease of collection and non-invasiveness. We observed that the existing approaches utilized either local or global features of facial images to diagnose ASD. However, its important to consider both local and global features to obtain fine-grained details and larger contextual information for accurate detection and classification. This paper proposes a sequencer-based patch-wise Local Feature Extractor along with a Global Feature Extractor. Finally, the features from these modules are aggregated to obtain the final feature for the classification of ASD. Experiments on a publicly available Autism Facial Image Dataset demonstrate that our proposed framework achieves state-of-the-art performance. We achieved accuracy, precision, recall, and F1-score of 94.7%, 94.0%, 95.3%, and 94.6%, respectively.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Systems, Man, and Cybernetics
Subtitle of host publicationImproving the Quality of Life, SMC 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1892-1897
Number of pages6
ISBN (Electronic)9798350337020
DOIs
Publication statusPublished - 2023
Event2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023 - Hybrid, Honolulu, United States
Duration: Oct 1 2023Oct 4 2023

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Conference

Conference2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023
Country/TerritoryUnited States
CityHybrid, Honolulu
Period10/1/2310/4/23

Keywords

  • and Global feature extraction
  • ASD
  • Autism Spectrum Disorder
  • classification
  • Local feature extraction
  • LSTM
  • Vision-transformer

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

Fingerprint

Dive into the research topics of 'Autism Spectrum Disorder Classification via Local and Global Feature Representation of Facial Image'. Together they form a unique fingerprint.

Cite this