Early diagnosis of Alzheimer's disease with deep learning

The accurate diagnosis of Alzheimer's disease (AD) plays a significant role in patient care, especially at the early stage, because the consciousness of the severity and the progression risks allows the patients to take prevention measures before irreversible brain damages are shaped. Although many studies have applied machine learning methods for computer-aided-diagnosis (CAD) of AD recently, a bottleneck of the diagnosis performance was shown in most of the existing researches, mainly due to the congenital limitations of the chosen learning models. In this study, we design a deep learning architecture, which contains stacked auto-encoders and a softmax output layer, to overcome the bottleneck and aid the diagnosis of AD and its prodromal stage, Mild Cognitive Impairment (MCI). Compared to the previous workflows, our method is capable of analyzing multiple classes in one setting, and requires less labeled training samples and minimal domain prior knowledge. A significant performance gain on classification of all diagnosis groups was achieved in our experiments.

1. INTRODUCTION

Alzheimer's disease (AD) is the most common form of dementia, which is a progressive brain disorder mostly occurring in the late life [1]. Comparing with the patient's previous functions, a decline in memory and other cognitive functions is noted as a primary dementia syndrome. In 2006, the worldwide prevalence of AD was 26.6 million, and this number is expected to double in every 20 years. By 2046, 1.2% of the global population will be affected by AD [2]. The early diagnosis of AD is primarily associated to the detection of Mild Cognitive Impairment (MCI), a prodromal stage of AD. Though the memory complaints and deficits of MCI do not notably affect the patients' daily activities, it has been reported that MCI has a high risk of progression to AD or other forms of dementia [3]. The accurate early diagnosis AD, especially identifying the risk of progression of MCI to AD, affords the AD patients awareness of the severity and allows them to take prevention measures, e.g., lifestyle changing and medications [4].