Application of Deep Learning in Classification and Diagnosis of Mild Cognitive Impairment

Abstract

Alzheimer's disease is an irreversible neurodegenerative disease that has not been completely cured, but its progression can be delayed by early intervention. Mild cognitive impairment is the initial stage of Alzheimer??s disease. It is of great significance to correctly identify this stage for early diagnosis and early intervention of Alzheimer??s disease. Deep learning has become a research hotspot in assisting the classification and diagnosis of mild cognitive impairment because it can automatically extract image features. In order to better classify mild cognitive impairment, this paper reviews the classification and diagnosis of mild cognitive impairment based on deep learning in recent years. Firstly, the commonly used datasets in the classification and diagnosis of mild cognitive impairment are introduced, and the data quantity, data type and download address of each dataset are sorted out. Secondly, this paper  summarizes the commonly used data preprocessing methods and model evaluation indicators. Then it focuses on the application of deep learning models and methods in the classification and diagnosis of mild cognitive impairment, including but not limited to automatic encoders, deep belief networks, generative adversarial networks, convolutional neural networks, and graph convolutional neural networks, and points out the model interpretability techniques used in the research. Finally, the main ideas, advantages and disadvantages of various algorithms are summarized, and the classification and diagnosis performance of mild cognitive impairment classification methods based on deep learning on public datasets is compared. The shortcomings in related research are summarized, and the future research direction is prospected.

Keywords

• mild cognitive impairment; deep learning; alzheimer's disease; classification diagnosis