Deep Neural Network Classification method to Alzheimer?s Disease Detection

Abstract

Early detection of Alzheimer disease (AD) is important for the management of disease. The human brain Magnetic resonance imaging (MRI) data have been used to detection of Alzheimer disease detection. The detection of AD is quite challenging and thus an automated tool to classify AD can be useful. Deep learning can make major advances in solving such problems. In this study, the longitudinal MRI data in non-demented and demented older adults data is utilized and the image processing technique was adopted for the data segmentation and attribute selection. Finally, deep neural network (DNN) classification is implemented for AD detection. The DNN 96.6% correctly identified AD and the minimum error rate obtained from a DNN. It shows the DNN will be useful for the development of improved computer aided diagnosis tool in MRI data.