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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 3-D convolutional neural network (3-D-CNN), and a kernel ridge regression-based random vector functional link (KRR-RVFL) network. We used 480 brain MRI images from the publicly availabel 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 3-D-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.

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