基于卷积神经网络和集成学习的阿尔茨海默症早期诊断  

Early prognosis of Alzheimer’s disease based on convolutional neural networks and ensemble learning

作　　者：曾安[1,2] 贾龙飞 潘丹 Song Xiaowei[5] ZENG An;JIA Longfei;PAN Dan;SONG Xiaowei(School of Computers, Guangdong University of Technology, Guangzhou 510006, P.R.China;Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou 510006, P.R.China;Modern Educational Technology Center, Guangdong Construction Polytechnic, Guangzhou 510440, P.R.China;Guangzhou Dazhi Networks Technology Co. Ltd., Guangzhou 510000, P.R.China;ImageTech Lab, Simon Fraser University, Vancouver V6B 5K3, Canada)

机构地区：[1]广东工业大学计算机学院,广州510006 [2]广东省大数据分析与处理重点实验室,广州510006 [3]广东建设职业技术学院现代教育技术中心,广州510440 [4]广州市大智网络科技有限公司,广州510000 [5]西蒙弗雷泽大学影像技术实验室,温哥华V6B5K3

出　　处：《生物医学工程学杂志》2019年第5期711-719,共9页Journal of Biomedical Engineering

基　　金：国家自然科学基金项目(61976058,61772143,61300107);广东省自然科学基金项目(S2012010010212);广州市科技计划项目(201601010034,201804010278);广东省大数据分析与处理重点实验室开放基金项目资助(201801)

摘　　要：阿尔茨海默症(AD)是一种典型的神经退行性疾病,临床上表现为失忆、丧失语言能力、丧失生活自理能力等。迄今为止,AD病因尚不明确且病程不可逆,也没有治愈的方法,因此,AD的早期诊断对于研发新型药物和措施以减缓病情发展具有重要意义。轻度认知障碍(MCI)是一种介于AD和正常老化(HC)之间的状态。研究表明,MCI患者比没有患过MCI的人更有可能发展成AD,因此,对MCI患者的准确筛查成为了AD早期诊断的研究热点之一。随着神经影像技术和深度学习的飞速发展,越来越多的研究者使用深度学习方法对大脑神经影像如磁共振影像(MRI)进行分析,用于AD的早期诊断。于是,本文提出基于卷积神经网络(CNN)和集成学习的多切片集成分类模型用于AD早期诊断。与只用单切片训练获得的CNN分类模型相比,本文采用三个维度上的多个二维切片进行训练而获得的集成分类器模型,能更充分地利用MRI包含的有效信息,从而提高分类的准确率和稳定性。Alzheimer’s disease(AD) is a typical neurodegenerative disease, which is clinically manifested as amnesia, loss of language ability and self-care ability, and so on. So far, the cause of the disease has still been unclear and the course of the disease is irreversible, and there has been no cure for the disease yet. Hence, early prognosis of AD is important for the development of new drugs and measures to slow the progression of the disease. Mild cognitive impairment(MCI) is a state between AD and healthy controls(HC). Studies have shown that patients with MCI are more likely to develop AD than those without MCI. Therefore, accurate screening of MCI patients has become one of the research hotspots of early prognosis of AD. With the rapid development of neuroimaging techniques and deep learning,more and more researchers employ deep learning methods to analyze brain neuroimaging images, such as magnetic resonance imaging(MRI), for early prognosis of AD. Hence, in this paper, a three-dimensional multi-slice classifiers ensemble based on convolutional neural network(CNN) and ensemble learning for early prognosis of AD has been proposed. Compared with the CNN classification model based on a single slice, the proposed classifiers ensemble based on multiple two-dimensional slices from three dimensions could use more effective information contained in MRI to improve classification accuracy and stability in a parallel computing mode.

关 键 词：阿尔茨海默症 轻度认知障碍 卷积神经网络 集成学习 磁共振图像 

分 类 号：R749.16[医药卫生—神经病学与精神病学]