  Computer-aided early diagnosis of Alzheimers Disease (AD) and its prodromal
form, Mild Cognitive Impairment (MCI), has been the subject of extensive
research in recent years. Some recent studies have shown promising results in
the AD and MCI determination using structural and functional Magnetic Resonance
Imaging (sMRI, fMRI), Positron Emission Tomography (PET) and Diffusion Tensor
Imaging (DTI) modalities. Furthermore, fusion of imaging modalities in a
supervised machine learning framework has shown promising direction of
research.
  In this paper we first review major trends in automatic classification
methods such as feature extraction based methods as well as deep learning
approaches in medical image analysis applied to the field of Alzheimer's
Disease diagnostics. Then we propose our own algorithm for Alzheimer's Disease
diagnostics based on a convolutional neural network and sMRI and DTI modalities
fusion on hippocampal ROI using data from the Alzheimers Disease Neuroimaging
Initiative (ADNI) database (http://adni.loni.usc.edu). Comparison with a single
modality approach shows promising results. We also propose our own method of
data augmentation for balancing classes of different size and analyze the
impact of the ROI size on the classification results as well.
