A Novel Hybrid Unet-RBF and CNN-RBF Algorithm for Autism Spectrum Disorder Classification
DOI:
https://doi.org/10.33736/jcshd.6778.2024Keywords:
autism spectrum disorder, convolutional neural network, deep learning, radial basis function, U-NetAbstract
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.
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