  Alzheimer's disease (AD) is an irreversible, progressive neuro degenerative
disorder that slowly destroys memory and thinking skills and eventually, the
ability to carry out the simplest tasks. In this paper, a deep neural network
based prediction of AD from magnetic resonance images (MRI) is proposed. The
state of the art image classification networks like VGG, residual networks
(ResNet) etc. with transfer learning shows promising results. Performance of
pre-trained versions of these networks are improved by transfer learning.
ResNet based architecture with large number of layers is found to give the best
result in terms of predicting different stages of the disease. The experiments
are conducted on Kaggle dataset.
