Deep Learning-Driven Enhanced Model for Autism Spectrum Disorder Diagnosis

Abhishek Kumre; Ajay Sourashtriya; Anurag Shrivastava

doi:10.71097/IJSAT.v16.i3.7195

Deep Learning-Driven Enhanced Model for Autism Spectrum Disorder Diagnosis

Author(s)	Abhishek Kumre, Ajay Sourashtriya, Anurag Shrivastava
Country	India
Abstract	Autism Spectrum Disorder (ASD) is a complex neurodevelopment condition characterized by impairments in social communication, restricted interests, and repetitive behaviors. Early and accurate diagnosis of ASD is crucial for timely intervention and improved quality of life. Traditional diagnostic methods often rely on behavioral assessments, which can be subjective, time-consuming, and require expert involvement. To address these limitations, this research proposes a Deep Learning-Driven Enhanced Model for ASD diagnosis that leverages advanced feature extraction and classification techniques. The proposed framework integrates convolutional neural networks (CNN) for high-level feature representation with optimized deep architectures, ensuring robust detection from diverse input modalities such as facial imagery and behavioral datasets. Comprehensive tests on publicly accessible ASD datasets showed that the improved model outperformed traditional machine learning techniques in terms of performance. The model demonstrated its ability to help doctors detect ASD early on by achieving better accuracy, sensitivity, and specificity. In order to provide scalable and impartial ASD screening systems for both clinical and non-clinical settings, this study highlights the need of deep learning in creating trustworthy, automated diagnosis tools.
Keywords	Autism Spectrum Disorder, Machine Learning, Early Diagnosis, Neurodevelopment Disorder, Deep Learning
Field	Engineering
Published In	Volume 16, Issue 3, July-September 2025
Published On	2025-07-23
DOI	https://doi.org/10.71097/IJSAT.v16.i3.7195
Short DOI	https://doi.org/g9t2xr

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Deep Learning-Driven Enhanced Model for Autism Spectrum Disorder Diagnosis

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