A Deep Neural Network Approach for Early Diagnosis of Mild Cognitive Impairment Using Multiple Features

Abstract

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease that is progressive and can be characterized mostly by neuronal atrophy, amyloid deposition, accompanied by cognition, behavioral and psychological deficits. In the recent decade, a variety of machine learning algorithms have been explored and used for AD diagnosis, focusing on its subtle prodromal stage of mild cognitive impairment (MCI) to assess essential features that characterize its early manifestation and to plan for early treatment. However, diagnosis of early MCI (EMCI) remains most challenging as it is extremely difficult to delineate from cognitively normal controls (CN), and as a consequence, most of the classification algorithms for these two groups are mixed with low classification accuracy results. In this study, a machine learning approach based on deep neural network (DNN) has been proposed in order to detect AD in its early stage using multimodal imaging, combining magnetic resonance imaging (MRI), positron emission tomography (PET) and standard neuropsychological test scores. The proposed approach makes use of the optimization method of Adam to update the learning weights in order to improve its accuracy. The algorithm is able to classify cognitively normal control group from EMCI with an unprecedented accuracy of 84.0%. Although the focus here is distinguishing the two groups of CN and EMCI for early diagnosis and treatment planning, this study also shows how the proposed deep learning algorithm can be extended for multiclass classification involving CN and all the stages of EMCI, late MCI (LMCI) and AD.