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Applying Eye Tracking with Deep Learning Techniques for Early-Stage Detection of Autism Spectrum Disorders

Author

Listed:
  • Zeyad A. T. Ahmed

    (Department of Computer Science, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad 431004, India)

  • Eid Albalawi

    (School of Computer Science and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

  • Theyazn H. H. Aldhyani

    (Applied College in Abqaiq, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

  • Mukti E. Jadhav

    (Department of Computer Sciences, Shri Shivaji Science and Arts College, Chikhli Dist., Buldana 443201, India)

  • Prachi Janrao

    (Department of Artificial Intelligence & Data Science, Thakur College of Engineering & Technology, Kandiwali(E), Mumbai 400101, India)

  • Mansour Ratib Mohammad Obeidat

    (Applied College, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

Abstract

Autism spectrum disorder (ASD) poses a complex challenge to researchers and practitioners, with its multifaceted etiology and varied manifestations. Timely intervention is critical in enhancing the developmental outcomes of individuals with ASD. This paper underscores the paramount significance of early detection and diagnosis as a pivotal precursor to effective intervention. To this end, integrating advanced technological tools, specifically eye-tracking technology and deep learning algorithms, is investigated for its potential to discriminate between children with ASD and their typically developing (TD) peers. By employing these methods, the research aims to contribute to refining early detection strategies and support mechanisms. This study introduces innovative deep learning models grounded in convolutional neural network (CNN) and recurrent neural network (RNN) architectures, employing an eye-tracking dataset for training. Of note, performance outcomes have been realised, with the bidirectional long short-term memory (BiLSTM) achieving an accuracy of 96.44%, the gated recurrent unit (GRU) attaining 97.49%, the CNN-LSTM hybridising to 97.94%, and the LSTM achieving the most remarkable accuracy result of 98.33%. These outcomes underscore the efficacy of the applied methodologies and the potential of advanced computational frameworks in achieving substantial accuracy levels in ASD detection and classification.

Suggested Citation

  • Zeyad A. T. Ahmed & Eid Albalawi & Theyazn H. H. Aldhyani & Mukti E. Jadhav & Prachi Janrao & Mansour Ratib Mohammad Obeidat, 2023. "Applying Eye Tracking with Deep Learning Techniques for Early-Stage Detection of Autism Spectrum Disorders," Data, MDPI, vol. 8(11), pages 1-27, November.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:11:p:168-:d:1273934
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    References listed on IDEAS

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    1. Sukru Eraslan & Victoria Yaneva & Yeliz Yesilada & Simon Harper, 2019. "Web users with autism: eye tracking evidence for differences," Behaviour and Information Technology, Taylor & Francis Journals, vol. 38(7), pages 678-700, July.
    2. Hasan Alkahtani & Zeyad A. T. Ahmed & Theyazn H. H. Aldhyani & Mukti E. Jadhav & Ahmed Abdullah Alqarni, 2023. "Deep Learning Algorithms for Behavioral Analysis in Diagnosing Neurodevelopmental Disorders," Mathematics, MDPI, vol. 11(19), pages 1-18, October.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Bawer Khan & Sohail Masood Bhatti & Arslan Akram, 2024. "Autism Spectrum Disorder Detection in Children Via Deep Learning Models Based on Facial Images," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(1), pages 557-572.

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