  Alzheimer's disease (AD) is one of the most common public health issues the
world is facing today. This disease has a high prevalence primarily in the
elderly accompanying memory loss and cognitive decline. AD detection is a
challenging task which many authors have developed numerous computerized
automatic diagnosis systems utilizing neuroimaging and other clinical data. MRI
scans provide high-intensity visible features, making these scans the most
widely used brain imaging technique. In recent years deep learning has achieved
leading success in medical image analysis. But a relatively little
investigation has been done to apply deep learning techniques for the brain MRI
classification. This paper explores the construction of several deep learning
architectures evaluated on brain MRI images and segmented images. The idea
behind segmented images investigates the influence of image segmentation step
on deep learning classification. The image processing presented a pipeline
consisting of pre-processing to enhance the MRI scans and post-processing
consisting of a segmentation method for segmenting the brain tissues. The
results show that the processed images achieved a better accuracy in the binary
classification of AD vs. CN (Cognitively Normal) across four different
architectures. ResNet architecture resulted in the highest prediction accuracy
amongst the other architectures (90.83% for the original brain images and
93.50% for the processed images).
