AccScience Publishing / JCTR / Online First / DOI: 10.36922/JCTR025400068
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ORIGINAL RESEARCH ARTICLE

A dual-modal approach for detecting and classifying autism spectrum disorder using behavioral and facial features

M. S. Sankari1* ,  A. Kannammal1
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1 Department of Electronics and Communication, Faculty of Electronics and Communication Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India
JCTR, 025400068 https://doi.org/10.36922/JCTR025400068
Received: 30 September 2025 | Revised: 20 December 2025 | Accepted: 15 January 2026 | Published online: 5 February 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Background: Autism spectrum disorder (ASD) is a neurodevelopmental condition that significantly affects social connection, behavior, and knowledge acquisition. Despite increasing global prevalence, timely diagnosis remains challenging due to heterogeneity in clinical presentation. Aim: The aim of this study is to develop a dual-modal framework for early detection of ASD by analyzing behavioral assessment and image data. Methods: The proposed framework consists of two independent yet complementary modules. In the behavioral module, questionnaire responses and assessment data were analyzed using an artificial neural network classifier to predict the likelihood of ASD. In the visual module, facial images were analyzed using a DenseNet121-based deep learning model with transfer learning to detect ASD-related traits. Each module independently estimates ASD probability and categorizes severity levels. Results: The DenseNet121 model achieved strong performance in image-based ASD detection, with 91.16% (95% confidence interval [CI]: 87.8–94.2) accuracy, 91.2% (95% CI: 87.8–94.2) sensitivity, and 89.8% specificity, including when trained on a relatively small dataset. Independent training of the two modules may improve robustness and reduce modality-specific bias. Conclusion: The proposed framework demonstrates potential for enhancing early ASD detection using dual modalities. The findings support the use of deep learning-based approaches to improve detection accuracy. Relevance for patients: Early screening of ASD can facilitate timely interventions and personalized care strategies. This method offers a noninvasive, data-driven approach that may support caregivers and healthcare systems in informed decision-making, ultimately benefiting individuals with ASD and their families.

Keywords
Behavioral features
Facial features
Artificial neural network
Transfer learning
Machine learning
Deep learning model
Severity prediction
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
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Journal of Clinical and Translational Research, Electronic ISSN: 2424-810X Print ISSN: 2382-6533, Published by AccScience Publishing
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