Accurate Brain Age Prediction Through Advanced Preprocessing and 3D ResNet-50 Modeling

Accurate brain age prediction from structural magnetic resonance imaging (MRI) holds significant potential for advancing our understanding of the aging process and its effects on neural structures.In this paper, a robust preprocessing pipeline and two state-of-the-art 3D convolutional neural network architectures, 3D ResNet-50 and 3D DenseNet-121, were employed to develop and evaluate a brain age prediction model.The preprocessing steps included skull removal, spatial normalization to the Montreal Neurological Institute (MNI) template, and brain tissue segmentation into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF).These steps ensured consistency and accuracy in the input data.The experimental results demonstrated that the 3D ResNet-50 architecture achieved superior performance, with a mean absolute error (MAE) of 3.9 for individuals over 50 years of age, surpassing the MAE of 4.1 achieved by the 3D DenseNet-121 model.These findings validate the efficacy of the proposed preprocessing pipeline and highlight the critical role of tailored deep learning architectures in brain age prediction.Future research could further enhance prediction accuracy by integrating multimodal imaging data and exploring hybrid model architectures.