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All Journal Bulletin of Electrical Engineering and Informatics
Sh-Hussain, Hadrina
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Electrical & Electronics Engineering

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Early detection of autism spectrum disorder through hybrid deep learning and classical machine learning approaches Khairina Ahmad Khair, Aina; Mohd Yaakob Wan Bejuri, Wan; Murtadha Mohamad, Mohd; Kadar, Masne; Kadim, Zulaikha; Sh-Hussain, Hadrina; Wahyuni, Deasy; Kasmin, Fauziah; Hea Choon, Ngo; Jaya Kumar, Yogan
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.10905

Abstract

Early detection of autism spectrum disorder (ASD) is essential for timely intervention. This study presents a hybrid artificial intelligence framework for non-invasive ASD pre-screening using children’s coloring, drawing, and handwriting activities. The proposed framework combines deep convolutional neural networks (VGG16, ResNet50, and EfficientNetB0) as feature extractors with a support vector machine (SVM) classifier to distinguish four diagnostic categories: non-ASD, mild ASD, moderate ASD, and severe ASD. Experimental results demonstrate task-specific performance across architectures. ResNet50–SVM achieved perfect classification for coloring tasks, with 100% accuracy, precision, recall, and F1-score. VGG16–SVM performed best for drawing, achieving 88% accuracy and recall, 89% precision, and an F1-score of 87%. EfficientNetB0–SVM produced the highest handwriting performance, achieving 96% across all evaluation metrics. These findings demonstrate the potential of computer vision-based analysis of children’s expressive activities as an effective, non-invasive ASD pre-screening tool. Future work will focus on expanding dataset diversity and integrating multimodal behavioral cues to improve model generalization and clinical applicability.
Co-Authors Hea Choon, Ngo Jaya Kumar, Yogan Kadar, Masne Kadim, Zulaikha Kasmin, Fauziah Khairina Ahmad Khair, Aina Mohd Yaakob Wan Bejuri, Wan Murtadha Mohamad, Mohd Wahyuni, Deasy