卷积神经网络在阿尔茨海默病诊断中的应用研究

Research on the application of convolution neural network in the diagnosis of Alzheimer’s disease

随着深度学习技术在疾病诊断方面的广泛应用,尤其是卷积神经网络(CNN)在计算机视觉、图像处理方面的突出表现,越来越多的研究提出使用该算法实现阿尔茨海默病(AD)、轻度认知障碍(MCI)以及正常认知(CN)之间的诊断。本文系统地回顾了几种经典的卷积神经网络模型在该疾病不同阶段脑影像分析诊断方面的应用进展,进一步探讨了其存在的问题及研究方向,以期为该领域的研究提供一定的参考和借鉴。

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.