  Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI
through machine learning have been a subject of intense research in recent
years. Recent success of deep learning in computer vision have progressed such
research further. However, common limitations with such algorithms are reliance
on a large number of training images, and requirement of careful optimization
of the architecture of deep networks. In this paper, we attempt solving these
issues with transfer learning, where state-of-the-art architectures such as VGG
and Inception are initialized with pre-trained weights from large benchmark
datasets consisting of natural images, and the fully-connected layer is
re-trained with only a small number of MRI images. We employ image entropy to
select the most informative slices for training. Through experimentation on the
OASIS MRI dataset, we show that with training size almost 10 times smaller than
the state-of-the-art, we reach comparable or even better performance than
current deep-learning based methods.
