Paper Title
Comparative Analysis of Convolutional Neural Network Models for Autism Spectrum Disorder Detection.
Article Identifiers
Authors
Monisha S , Meghana G N , Deepthi S , Likitha H M , Pramod Naik
Keywords
Convolutional Neural Network, VGG-16, Nas net, Dense Net, Inception Net, Efficient Net, ResNet, AlexNet, Xception Net.
Abstract
Autism Spectrum Disorder (ASD) diagnosis presents significant challenges, often relying on clinical observation and expert evaluation. Leveraging machine learning, particularly Convolutional Neural Networks (CNNs), holds promise for automating ASD detection. This study scrutinizes the efficacy of various CNN architectures—VGG-16, NASNet, DenseNet, InceptionNet, EfficientNet, ResNet, AlexNet, and XceptionNet—for ASD detection. The study dwelve into conducting experiments on Autism image dataset, and rigorously test the performance metrics. The proposed EfficientNet model achieved the highest accuracy, 98%, compared to others models.
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How To Cite (APA)
Monisha S, Meghana G N, Deepthi S, Likitha H M, & Pramod Naik (May-2024). Comparative Analysis of Convolutional Neural Network Models for Autism Spectrum Disorder Detection.. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), f67-f79. https://ijnrd.org/papers/IJNRD2405509.pdf
Issue
Volume 9 Issue 5, May-2024
Pages : f67-f79
Other Publication Details
Paper Reg. ID: IJNRD_220941
Published Paper Id: IJNRD2405509
Downloads: 000121986
Research Area: Computer Science & TechnologyÂ
Country: Bangalore, Karnataka, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2405509.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405509
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