Deep Learning Algorithms for The Analysis of Autism Spectrum ‎Disorder

  • Authors

    • Sneha Sureddy Assistant Professor, BMS Institute of Technology and Management, Bengaluru, India
    • Dr. P. L. x Dr. P. L. Lekshmy Assistant Professor, Department of CSE, LBS Institute of Technology for Women, Kerala, India
    • Dr. Zameer Ahmed Adhoni Assistant Professor, Department of CSE, GITAM School of Technology, GITAM (Deemed to Be University, Nagadenahalli, ‎Doddabalapura, Bengaluru, India
    • Aakansha Soy Assistant Professor, Department of CS & IT, Kalinga University, Raipur, India
    • Ashu Nayak Assistant Professor, Department of CS & IT, Kalinga University, Raipur, India
    https://doi.org/10.14419/4cc23330

    Received date: June 10, 2025

    Accepted date: June 17, 2025

    Published date: November 1, 2025

  • Autism Spectrum Disorder; Computer-Aided Diagnostic Systems; Deep Learning; Wavelet Scattering Transform; AleXnet

  • Abstract

    The neurodevelopmental disorder known as autism spectrum disorder (ASD) is typified by aberrant brain development that is impacted by ‎environmental, biological, and genetic variables. To maximize outcomes and to intervene promptly, it is necessary to identify ASD early. ‎The current methods of screening rely heavily on behavioral assessments, which introduce bias and other limitations. Therefore, objective ‎approaches that mitigate subjectivity should be employed. This study presents using EEG signals and Deep Learning (DL) models to ‎identify ASD via Artificial Intelligence (AI)-based Computer-Aided Diagnostic Systems (CADS.‎ ‎ This study examined how well deep ‎learning classifiers (DL classifiers) could classify people with ASD using only their raw EEG data and no manual feature extraction. ‎Several deep learning models, such as FFNN and WST-ASDNet with AlexNet, were employed to categorize people with ASD and ‎controls. As additional signal processing methods for ASD classification, time-frequency representations, Power Spectral Density (PSD) ‎estimates, and Wavelet Scattering Transform (WST) were used. When AleXnet was combined with the other DL model, the suggested ‎Wavelet Scattering Transform-based ASD diagnostic networks (WST-ASDNets) produced accuracies of 98% and 95%, respectively, in ‎separating ASD from standard controls. The AI-based CADS created for ASD diagnosis showed encouraging outcomes and provided a ‎quicker and less labor-intensive method in terms of computation. These techniques have promise as screening instruments for the early ‎identification and classification of ASD. Given the differences in EEG signals among people with ASD, future research may concentrate on ‎expanding these algorithms to categorize ASD subjects into varying severity levels‎.

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  • How to Cite

    Sureddy, S. ., Dr. P. L. Lekshmy , D. P. L. x, Adhoni, D. . Z. A. ., Soy, A. ., & Nayak, A. . (2025). Deep Learning Algorithms for The Analysis of Autism Spectrum ‎Disorder. International Journal of Basic and Applied Sciences, 14(SI-1), 473-479. https://doi.org/10.14419/4cc23330