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

Zeyad A. T. Ahmed (), Eid Albalawi, Theyazn H. H. Aldhyani (), Mukti E. Jadhav, Prachi Janrao and Mansour Ratib Mohammad Obeidat
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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

Data, 2023, vol. 8, issue 11, 1-27

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.

Keywords: autism spectrum disorder; typically developing; deep learning; CNN; LSTM; GRU; eye-tracking technique (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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