CT Brain Image Classification Based on Deep Learning in Application of
Screening of Alzheimer Disease
HUI Rui1,2, GAO Xiaohong3, TIAN Zengmin1
1.Department of Neurosurgery, Navy General Hospital of the Chinese People’s Liberation Army, Beijing 100048, China;
2.Neurosurgery Department, Brigham and Women’s Hospital, Boston 02115, US;
3.Department of Computer Science, Middlesex University, London, NW4 4BT, UK
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.
基金资助:国家863计划(2007AA420100-1);European Union’s
Framework 7 research program under grant agreement (PIRSESGA-
2010-269124)。
作者简介:
引用本文:
惠瑞1,2,高小红3,田增民1. 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.
HUI Rui1,2, GAO Xiaohong3, TIAN Zengmin1. CT Brain Image Classification Based on Deep Learning in Application of
Screening of Alzheimer Disease. China Medical Devices, 2017, 32(12): 15-19.