A structural magnetic resonance image-based brain age deep learning prediction system, comprising: a training set construction module; a data preprocessing module; a deep learning model construction module which is used for constructing a convolutional neural network model AGE-DenseNet by using a Keras framework and on the basis of a DenseNet thought; a deep learning model training module; a model verification module; and a brain age prediction module. A DenseNet thought-based convolutional neural network structure AGE-DenseNet is adopted, high-dimensional complex features can be extracted from complex brain structural magnetic resonance images, the brain age can be accurately, efficiently and quickly predicted, and a degree that the brain deviates from the aging track of a healthy brain is quantified.
STRUCTURAL MAGNETIC RESONANCE IMAGE-BASED BRAIN AGE DEEP LEARNING PREDICTION SYSTEM