  Recently, machine learning techniques especially predictive modeling and
pattern recognition in biomedical sciences from drug delivery system to medical
imaging has become one of the important methods which are assisting researchers
to have deeper understanding of entire issue and to solve complex medical
problems. Deep learning is a powerful machine learning algorithm in
classification while extracting low to high-level features. In this paper, we
used convolutional neural network to classify Alzheimer's brain from normal
healthy brain. The importance of classifying this kind of medical data is to
potentially develop a predict model or system in order to recognize the type
disease from normal subjects or to estimate the stage of the disease.
Classification of clinical data such as Alzheimer's disease has been always
challenging and most problematic part has been always selecting the most
discriminative features. Using Convolutional Neural Network (CNN) and the
famous architecture LeNet-5, we successfully classified structural MRI data of
Alzheimer's subjects from normal controls where the accuracy of test data on
trained data reached 98.84%. This experiment suggests us the shift and scale
invariant features extracted by CNN followed by deep learning classification is
most powerful method to distinguish clinical data from healthy data in fMRI.
This approach also enables us to expand our methodology to predict more
complicated systems.
