Deep Learning Based Diagnosis of Alzheimer's Disease Using Structural Magnetic Resonance Imaging: A Survey

Alzheimer's disease (AD) has been regarded as the most common form of dementia which affects millions of people around the world. While there is no specific remedy for AD, an precise prediction in the early stage could effectively delay the onset of AD. Structural Magnetic Resonance Imaging (sMRI) can detect brain abnormalities for AD patients and has been widely used for AD diagnosis. Thanks to the rapid development of deep learning techniques, a great number of deep learning methods have been adopted to obtain task-orientated features from sMRI and achieved satisfactory performance. In this paper, we first systematically review these applications of deep learning models on AD detection using sMRI. Specifically, we divide them into four main categories according to their input types and discuss their advantages and limitations. Then, we propose two challenges for current studies: incomparable performance and the difficulty to efficiently model the relationship between spatially distant regions. Finally, we offer two possible future research directions for building better deep learning-based AD detection models.