  Over the past decade, 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 power learning machine learning
algorithm in classification while extracting 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 functional MRI data of
Alzheimer's subjects from normal controls where the accuracy of test data on
trained data reached 96.85%. 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.
