Xu Baohong, Ding Chong, Xu Guizhi
Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, P.R.China;State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2021 Feb 25;38(1):169-177. doi: 10.7507/1001-5515.202007019.
With the wide application of deep learning technology in disease diagnosis, especially the outstanding performance of convolutional neural network (CNN) in computer vision and image processing, more and more studies have proposed to use this algorithm to achieve the classification of Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal cognition (CN). This article systematically reviews the application progress of several classic convolutional neural network models in brain image analysis and diagnosis at different stages of Alzheimer's disease, and discusses the existing problems and gives the possible development directions in order to provide some references.
随着深度学习技术在疾病诊断中的广泛应用，尤其是卷积神经网络（CNN）在计算机视觉和图像处理方面的出色表现，越来越多的研究提出使用该算法来实现阿尔茨海默病（AD）、轻度认知障碍（MCI）和正常认知（CN）的分类。本文系统综述了几种经典卷积神经网络模型在阿尔茨海默病不同阶段脑图像分析与诊断中的应用进展，并讨论了存在的问题，给出了可能的发展方向，以期提供一些参考。
