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 data sets in the classification and diagnosis of mild cognitive impairment are introduced, and the data quantity, data type and download address of each data set are sorted out. Secondly, summarize 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 data sets is compared. The shortcomings in related research are summarized, and the future research direction is prospected.

Key words: mild cognitive impairment, deep learning, Alzheimer's disease, classification diagnosis

摘要： 阿尔兹海默症是一种不可逆的神经退行性疾病，至今尚无彻底治愈可能，但可通过早期干预延缓其进展。轻度认知障碍是阿尔兹海默症的初始阶段，正确识别该阶段对阿尔兹海默症早期诊断继而进行早期干预意义重大。深度学习因其能够自动提取图像特征，目前已成为辅助轻度认知障碍分类诊断的研究热点。为了更好地对轻度认知障碍进行分类研究，本文对近年来的基于深度学习的轻度认知障碍分类诊断论文进行回顾。首先介绍轻度认知障碍分类诊断中常用数据集，整理了各数据集数据数量、数据类型及下载地址。其次，总结常用的数据预处理方式以及模型评价指标。后重点介绍了深度学习模型与方法在轻度认知障碍分类诊断中的应用，包括但不限于自动编码器、深度置信网络、生成对抗网络、卷积神经网络、图卷积神经网络，并指出研究中所使用的模型可解释性技术。最后，总结了各种算法的主要思想及优缺点，并对比了基于深度学习的轻度认知障碍分类方法在公开数据集上的分类诊断表现，归纳出相关研究中尚存的不足，并对未来研究方向进行展望。

关键词: 轻度认知障碍, 深度学习, 阿尔兹海默症, 分类诊断