Jul 2026· International Conference Computing Methodologies and Communication· pp. 1297-1304· 0 citations· 17 references
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that manifests itself through unusual social interactions and visual attention. Thus, it is of prime importance to diagnose the condition correctly. In this context, this paper proposes a deep learning-based framework for the detection of Autism Spectrum Disorder by utilizing eye-tracking images. The structured framework of the paper is as follows: the images are first preprocessed through a structured framework involving resizing, min-max normalization, and Otsu threshold-based segmentation. Then, rotation-based data augmentation is performed on the images. Finally, the EfficientNet-B4 network is used in combination with Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) for the detection of ASD. The experimental results of the framework have shown that the framework has achieved a classification accuracy of 98.1%, thereby outperforming than other state-of-the-art techniques.
This study investigates the utilization of deep learning models to recognize ASD among 13-year-old children based on eye movement data collected as participants observed static images and short video sequences, highlighting the potential of deep learning frameworks as objective, data-driven tools for ASD detection in b...
Muhamad Syukron, R. Faresta· Jurnal Ilmiah Kursor· 0 citations
Autism Spectrum Disorder (ASD) is a prevalent neurodevelopmental condition for which early screening is essential to support timely intervention. Conventional diagnostic procedures are often time-consuming and resource-intensive, highlighting the need for accessible and cost-effective screening approaches. This study i...
Autism, or autism spectrum disorder (ASD), is a neurodevelopmental condition characterized by atypical development of neurons in the brain. Diagnosing ASD remains a major challenge due to the complexity and heterogeneity of the symptoms associated with this disorder. Early detection is essential, as rapid treatment sig...
Fedia Bahloul, Raouia Mokni, M. Kherallah· International Conference on...· 0 citations
The proposed VGG16-based approach has potential as a supportive, non-invasive tool for early ASD screening and is deployed as an interactive, Streamlit-based web application that allows users to upload facial images and receive real-time predictions.
Experimental evaluation on the proposed multi-head Attention-driven Multimodal Feature Integration Network framework demonstrates that the proposed MAtMFIN framework consistently outperforms state-of-the-art transfer learning and Deep Learning models, including a hybrid Convolutional Neural Network (CNN)-Long Short-Ter...
A. R., Radha Senthilkumar· Discover Artificial Intellig...· 0 citations
These findings demonstrate the potential of computer vision-based analysis of children’s expressive activities as an effective, non-invasive ASD pre-screening tool and will focus on expanding dataset diversity and integrating multimodal behavioral cues to improve model generalization and clinical applicability.
Aina Khairina Ahmad Khair, Wan Mohd Yaakob Wan Bejuri, Mohd Murtadha Mohamad et al.· Bulletin of Electrical Engin...· 0 citations
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