Development of Hybrid Convolutional Neural Network and Radial Basis Function for Autism Spectrum Disorder Classification

Autism spectrum disorder (ASD) has become a common topic. The symptoms and heterogeneity of individuals with ASD change over time. In addition, the assessment using rs-fMRI data to classify ASD fails to attain the biomarker standards, and the obstacle still does not settle in the previous study. Acc...

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Main Author: Huey Chern, Lim
Format: Thesis
Language:English
Published: 2024
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Online Access:http://ir.unimas.my/id/eprint/45966/1/Lim%20Huey%20Chern%20MSc%20Thesis%20FCS%20Final_rev%20AASAH%201.pdf
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spelling my-unimas-ir.459662024-09-06T08:38:58Z Development of Hybrid Convolutional Neural Network and Radial Basis Function for Autism Spectrum Disorder Classification 2024-09-05 Huey Chern, Lim Q Science (General) Autism spectrum disorder (ASD) has become a common topic. The symptoms and heterogeneity of individuals with ASD change over time. In addition, the assessment using rs-fMRI data to classify ASD fails to attain the biomarker standards, and the obstacle still does not settle in the previous study. According to the Centers for Disease Control and Prevention (CDC) report, in 2021, the prevalence rate of ASD will increases to 1 in 44 children. Therefore, further methods for higher classification performance must be investigated. Deep learning has recently dramatically improved the cutting edge in a wide range of artificial intelligence jobs. Hence, this study proposed hybrid deep learning algorithms for ASD classification. Two algorithms merged: U-net neural network and Radial Basis Function (RBF) for medical image segmentation. Besides, the convolutional neural network (CNN) was implemented and combined with the RBF algorithm for the ASD classification. In this research, the classification of ASD achieved 94.79% accuracy, a better result compared with previous research. This study also illustrates the applicability of deep learning methods in neuroscience research projects. Future research should explore diverse algorithms, validating them across varied datasets with different hyperparameters to enhance ASD classification efficiency. UNIMAS 2024-09 Thesis http://ir.unimas.my/id/eprint/45966/ http://ir.unimas.my/id/eprint/45966/1/Lim%20Huey%20Chern%20MSc%20Thesis%20FCS%20Final_rev%20AASAH%201.pdf text en public masters Faculty of Cognitive Science and Human Development Cognitive Science
institution Universiti Malaysia Sarawak
collection UNIMAS Institutional Repository
language English
topic Q Science (General)
spellingShingle Q Science (General)
Huey Chern, Lim
Development of Hybrid Convolutional Neural Network and Radial Basis Function for Autism Spectrum Disorder Classification
description Autism spectrum disorder (ASD) has become a common topic. The symptoms and heterogeneity of individuals with ASD change over time. In addition, the assessment using rs-fMRI data to classify ASD fails to attain the biomarker standards, and the obstacle still does not settle in the previous study. According to the Centers for Disease Control and Prevention (CDC) report, in 2021, the prevalence rate of ASD will increases to 1 in 44 children. Therefore, further methods for higher classification performance must be investigated. Deep learning has recently dramatically improved the cutting edge in a wide range of artificial intelligence jobs. Hence, this study proposed hybrid deep learning algorithms for ASD classification. Two algorithms merged: U-net neural network and Radial Basis Function (RBF) for medical image segmentation. Besides, the convolutional neural network (CNN) was implemented and combined with the RBF algorithm for the ASD classification. In this research, the classification of ASD achieved 94.79% accuracy, a better result compared with previous research. This study also illustrates the applicability of deep learning methods in neuroscience research projects. Future research should explore diverse algorithms, validating them across varied datasets with different hyperparameters to enhance ASD classification efficiency.
format Thesis
qualification_level Master's degree
author Huey Chern, Lim
author_facet Huey Chern, Lim
author_sort Huey Chern, Lim
title Development of Hybrid Convolutional Neural Network and Radial Basis Function for Autism Spectrum Disorder Classification
title_short Development of Hybrid Convolutional Neural Network and Radial Basis Function for Autism Spectrum Disorder Classification
title_full Development of Hybrid Convolutional Neural Network and Radial Basis Function for Autism Spectrum Disorder Classification
title_fullStr Development of Hybrid Convolutional Neural Network and Radial Basis Function for Autism Spectrum Disorder Classification
title_full_unstemmed Development of Hybrid Convolutional Neural Network and Radial Basis Function for Autism Spectrum Disorder Classification
title_sort development of hybrid convolutional neural network and radial basis function for autism spectrum disorder classification
granting_institution Faculty of Cognitive Science and Human Development
granting_department Cognitive Science
publishDate 2024
url http://ir.unimas.my/id/eprint/45966/1/Lim%20Huey%20Chern%20MSc%20Thesis%20FCS%20Final_rev%20AASAH%201.pdf
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