中国医疗设备2017，Vol.32Issue(12)：15-19,5.DOI:10.3969/j.issn.1674-1633.2017.12.004
基于深度学习的CT脑影像分类方法用于阿尔茨海默病的初步筛查
CT Brain Image Classification Based on Deep Learning in Application of Screening of Alzheimer Disease
摘要
Abstract
Objective The study aims to discuss the application of deep leaning based on the convolutional neural network (CNN) in the CT imaging classification, so as to improve the intelligent image classification for clinical screening of Alzheimer disease (AD). Methods Three categories of brain CT image data, including the data from AD patients, organic lesion patients (eg. tumor, cerebral hemorrhage) and normal aging patients were collected. For the reason that the relative horizontal direction in CT brain image was high (z axis, seam thickness 5 mm), we fused the two dimensional and three dimensional CNN data in this study, and the results were compared with the diagnostic results. Results The accuracy rates of diagnosis for AD patients, organic lesion patients and normal aging patients were 84.2%, 73.9% and 88.9% respectively, with mean rate of 82.3%. Conclusion Our results supply a new method for preliminary screen of AD.关键词
卷积神经网络/图像分类/CT影像/阿尔茨海默病Key words
convolutional neural network/image classification/CT image/Alzheimer disease分类
医药卫生引用本文复制引用
惠瑞,高小红,田增民..基于深度学习的CT脑影像分类方法用于阿尔茨海默病的初步筛查[J].中国医疗设备,2017,32(12):15-19,5.基金项目
国家863计划(2007AA420100-1) （2007AA420100-1）
European Union's Framework 7 research program under grant agreement(PIRSES-GA-2010-269124). （PIRSES-GA-2010-269124）
