Computer-aided Brain Age Estimation via Ensemble Learning of 3D Convolutional Neural Networks

Abstract

predicting brain age using Magnetic Resonant Imaging (MRI) and its difference with chronological age is useful for detecting Alzheimer's disease in the early stages. For having accurate brain age prediction with MRI, Deep learning could play an active role, but its performance is highly dependent on the amount of data and computes memory we access. In this paper, in order to approximate as accurately as possible, the age of the brain through T1 weighted structural MRI, a deep 3D convolutional neural network model is proposed. Furthermore, different techniques such as data normalization and ensemble learning have been applied to the suggested model for getting more accurate results. The system is trained and tested on the IXI database, which is being normalized by SPM12. Finally, this model is assessed through the Mean Absolute Error (MAE) metric, and the results demonstrate our model is capable of computing the approximation age of the subjects with an MAE, which is equal to 5.07 years.