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

Author:

Ahmed Zeyad A. T.1ORCID,Albalawi Eid2,Aldhyani Theyazn H. H.3ORCID,Jadhav Mukti E.4ORCID,Janrao Prachi5ORCID,Obeidat Mansour Ratib Mohammad6

Affiliation:

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

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

3. Applied College in Abqaiq, King Faisal University, Al-Ahsa 31982, Saudi Arabia

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

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

6. 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.

Funder

King Faisal University

Publisher

MDPI AG

Subject

Information Systems and Management,Computer Science Applications,Information Systems

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