Early detection of Alzheimer’s disease based on the state-of-the-art deep learning approach: a comprehensive survey

Abstract

Alzheimer’s disease (AD) is a form of brain disorder that causes functions’ loss in a person’s daily activity. Due to the tremendous progress of Alzheimer’s patients and the lack of accurate diagnostic tools, early detection and classification of Alzheimer’s disease are open research areas. Accurate detection of Alzheimer’s disease in an effective way is one of the many researchers’ goals to limit or overcome the disease progression. The main objective of the current survey is to introduce a comprehensive evaluation and analysis of the most recent studies for AD early detection and classification under the state-of-the-art deep learning approach. The article provides a simplified explanation of the system stages such as imaging, preprocessing, learning, and classification. It addresses broad categories of structural, functional, and molecular imaging in AD. The included modalities are magnetic resonance imaging (MRI; both structural and functional) and positron emission tomography (PET; for assessment of both cerebral metabolism and amyloid). It reviews the process of pre-processing techniques to enhance the quality. Additionally, the most common deep learning techniques used in the classification process will be discussed. Although deep learning with preprocessing images has achieved high performance as compared to other techniques, there are some challenges. Moreover, it will also review some challenges in the classification and preprocessing image process over some articles what they introduce, and techniques used, and how they solved these problems.