Kernelized Random Vector Functional-Link Network

Borah, Parashjyoti; Gupta, Deepak; Mishra, Sandeep Soumya Sekhar

doi:10.1007/978-981-19-1520-8_60

Cited by 2 publications

(1 citation statement)

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“…The input features are directly fed to the output layer, which generates outputs employing a linear decision function. In this study, a kernel trick is applied to a standard RVFL network, which transforms the input space into higher dimensional feature space while supplying input features to the RVFL output layer through direct links [4], [38]. As a result, the output layer of the kernel ridge regression-based RVFL (KRR-RVFL) network gets complete nonlinearity by using the synergies of nonlinear activation functions like Radbas, Tribas, sigmoid, and kernel function.…”

Section: Introductionmentioning

confidence: 99%

Kernel-Ridge-Regression-Based Randomized Network for Brain Age Classification and Estimation

Pilli,

Goel,

Murugan

et al. 2024

IEEE Trans. Cogn. Dev. Syst.

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Accelerated brain aging and abnormalities are associated with variations in brain patterns. Effective and reliable assessment methods are required to accurately classify and estimate brain age. In this study, a brain age classification and estimation framework is proposed using structural magnetic resonance imaging (sMRI) scans, a 3D convolutional neural network (3D-CNN), and a kernel ridge regression-based random vector functional link (KRR-RVFL) network. We used 480 brain MRI images from the publicly available IXI database and segmented them into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) images to show age-related associations by region. Features from MRI images are extracted using 3D-CNN and fed into the wavelet KRR-RVFL network for brain age classification and prediction. The proposed algorithm achieved high classification accuracy, 97.22%, 99.31%, and 95.83% for GM, WM, and CSF regions, respectively. Moreover, the proposed algorithm demonstrated excellent prediction accuracy with a mean absolute error (MAE) of 3.89 years, 3.64 years, and 4.49 years for GM, WM, and CSF regions, confirming that changes in WM volume are significantly associated with normal brain aging. Additionally, voxel-based morphometry (VBM) examines age-related anatomical alterations in different brain regions in GM, WM, and CSF tissue volumes.

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Section: Introductionmentioning

confidence: 99%