A Novel Hybrid Unet-RBF and CNN-RBF Algorithm for Autism Spectrum Disorder Classification
The 2021 CDC report indicates that Autism Spectrum Disorder affects 1 in 44 children, necessitating advanced classification methods. This article proposes a hybrid deep learning approach for ASD classification, merging U-net and Radial Basis Functions for medical image segmentation and integrating C...
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| Online Access: | http://ir.unimas.my/id/eprint/44530/3/A%20Novel.pdf http://ir.unimas.my/id/eprint/44530/ https://publisher.unimas.my/ojs/index.php/JCSHD/article/view/6778 https://doi.org/10.33736/jcshd.6778.2024 |
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my.unimas.ir.445302024-04-01T06:10:32Z http://ir.unimas.my/id/eprint/44530/ A Novel Hybrid Unet-RBF and CNN-RBF Algorithm for Autism Spectrum Disorder Classification Lim, Huey Chern Abdulrazak Yahya, Saleh QA76 Computer software The 2021 CDC report indicates that Autism Spectrum Disorder affects 1 in 44 children, necessitating advanced classification methods. This article proposes a hybrid deep learning approach for ASD classification, merging U-net and Radial Basis Functions for medical image segmentation and integrating Convolutional Neural Network with RBF for ASD classification. Achieving 94.79% accuracy surpasses previous studies, highlighting deep learning's potential in neuroscience. Future research should explore diverse algorithms, validating them across varied datasets with different hyperparameters to enhance ASD classification efficiency. UNIMAS Publisher 2024-03-31 Article PeerReviewed text en http://ir.unimas.my/id/eprint/44530/3/A%20Novel.pdf Lim, Huey Chern and Abdulrazak Yahya, Saleh (2024) A Novel Hybrid Unet-RBF and CNN-RBF Algorithm for Autism Spectrum Disorder Classification. Journal of Cognitive Sciences and Human Development, 10 (1). pp. 87-102. ISSN 2550-1623 https://publisher.unimas.my/ojs/index.php/JCSHD/article/view/6778 https://doi.org/10.33736/jcshd.6778.2024 |
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QA76 Computer software Lim, Huey Chern Abdulrazak Yahya, Saleh A Novel Hybrid Unet-RBF and CNN-RBF Algorithm for Autism Spectrum Disorder Classification |
| description |
The 2021 CDC report indicates that Autism Spectrum Disorder affects 1 in 44 children, necessitating advanced classification methods. This article proposes a hybrid deep learning approach for ASD classification, merging U-net and Radial Basis Functions for medical image segmentation and integrating Convolutional Neural Network with RBF for ASD classification. Achieving 94.79% accuracy surpasses
previous studies, highlighting deep learning's potential in neuroscience. Future research should explore diverse algorithms, validating them across varied datasets with different hyperparameters to enhance ASD
classification efficiency. |
| format |
Article |
| author |
Lim, Huey Chern Abdulrazak Yahya, Saleh |
| author_facet |
Lim, Huey Chern Abdulrazak Yahya, Saleh |
| author_sort |
Lim, Huey Chern |
| title |
A Novel Hybrid Unet-RBF and CNN-RBF Algorithm for Autism
Spectrum Disorder Classification |
| title_short |
A Novel Hybrid Unet-RBF and CNN-RBF Algorithm for Autism
Spectrum Disorder Classification |
| title_full |
A Novel Hybrid Unet-RBF and CNN-RBF Algorithm for Autism
Spectrum Disorder Classification |
| title_fullStr |
A Novel Hybrid Unet-RBF and CNN-RBF Algorithm for Autism
Spectrum Disorder Classification |
| title_full_unstemmed |
A Novel Hybrid Unet-RBF and CNN-RBF Algorithm for Autism
Spectrum Disorder Classification |
| title_sort |
novel hybrid unet-rbf and cnn-rbf algorithm for autism
spectrum disorder classification |
| publisher |
UNIMAS Publisher |
| publishDate |
2024 |
| url |
http://ir.unimas.my/id/eprint/44530/3/A%20Novel.pdf http://ir.unimas.my/id/eprint/44530/ https://publisher.unimas.my/ojs/index.php/JCSHD/article/view/6778 https://doi.org/10.33736/jcshd.6778.2024 |
| _version_ |
1795293756887400448 |
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13.211869 |