A Deep Learning Model in the Detection of Alzheimer Disease

Abstract

Precise detection of Alzheimer's disease (AD) plays an important role in health treatment, particularly at an early stage, since understanding of the likelihood of incidence and development helps patients to take preventive steps before permanent brain damage is induced. While several experiments have recently used machine learning approaches for the computer-aided diagnosis of AD, a bottleneck in diagnostic performance has been found in most of the previous studies, mainly due to the congenital defects of the selected learning models. In this research paper, to resolve the bottleneck and help diagnose AD and its prodromal level, Mild Cognitive Impairment (MCI), with stacked auto-encoders and a softmax output layer, we have created a deep learning architecture. Compared to previous workflows, our approach is capable of evaluating a variety of groups in a single setting that involve fewer labeled training samples and limited prior domain awareness. A substantial improvement in efficiency was achieved in the description of all diagnostic classes in this research paper.