  Alzheimer's disease (AD) is an irreversible neurode generative disease of the
brain.The disease may causes memory loss, difficulty communicating and
disorientation. For the diagnosis of Alzheimer's disease, a series of scales
are often needed to evaluate the diagnosis clinically, which not only increases
the workload of doctors, but also makes the results of diagnosis highly
subjective. Therefore, for Alzheimer's disease, imaging means to find early
diagnostic markers has become a top priority.
  In this paper, we propose a novel 3DMgNet architecture which is a unified
framework of multigrid and convolutional neural network to diagnose Alzheimer's
disease (AD). The model is trained using an open dataset (ADNI dataset) and
then test with a smaller dataset of ours. Finally, the model achieved 92.133%
accuracy for AD vs NC classification and significantly reduced the model
parameters.
