  Pattern recognition methods using neuroimaging data for the diagnosis of
Alzheimer's disease have been the subject of extensive research in recent
years. In this paper, we use deep learning methods, and in particular sparse
autoencoders and 3D convolutional neural networks, to build an algorithm that
can predict the disease status of a patient, based on an MRI scan of the brain.
We report on experiments using the ADNI data set involving 2,265 historical
scans. We demonstrate that 3D convolutional neural networks outperform several
other classifiers reported in the literature and produce state-of-art results.
