[1]周文,王瑜,李长胜,等. LightGBM算法在阿尔茨海默症结构磁共振成像分类中的应用[J].中国医学物理学杂志,2019,36(4):408-413.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.008]
　ZHOU Wen,WANG Yu,LI Changsheng,et al. Application of LightGBM algorithm in classification of patients with Alzheimer’s disease from structural magnetic resonance images[J].Chinese Journal of Medical Physics,2019,36(4):408-413.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.008]
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 LightGBM算法在阿尔茨海默症结构磁共振成像分类中的应用()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
36卷
期数:
2019年第4期
页码:
408-413
栏目:
医学影像物理
出版日期:
2019-04-25

文章信息/Info

Title:
 Application of LightGBM algorithm in classification of patients with Alzheimer’s disease from structural magnetic resonance images
文章编号:
1005-202X（2019）04-0408-06
作者:
 周文; 王瑜; 李长胜; 肖洪兵; 邢素霞
 北京工商大学计算机与信息工程学院食品安全大数据技术北京市重点实验室， 北京 100048
Author(s):
 ZHOU Wen;  WANG Yu;  LI Changsheng;  XIAO Hongbing;  XING Suxia
 Key Laboratory of Food Safety Big Data Technology, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
关键词:
阿尔茨海默症; LightGBM算法; 结构磁共振成像; 病灶脑区
Keywords:
 ; Keywords: Alzheimer’s disease;  LightGBM algorithm;  structural magnetic resonance image;  brain lesion
分类号:
R318；R749.16
DOI:
DOI:10.3969/j.issn.1005-202X.2019.04.008
文献标志码:
A
摘要:
 为更好地利用计算机技术分析阿尔茨海默症（AD）患者的大脑脑区变化，并对AD进行辅助诊断，本研究选择来自AD神经影像数据库的116名AD患者、116名轻度认知障碍患者和117名正常对照者的脑部结构磁共振成像，并利用spm软件对3组数据进行预处理和统计学相关性分析，得到差异性脑区。然后使用IBASPM软件提取病灶脑区体积作为特征样本。最后利用LightGBM算法对特征向量进行分类，并与支持向量机和XGBoost算法作对比实验。实验结果显示，利用LightGBM算法对病灶脑区的体积进行分类，准确率可达到83%。在这3种分类算法中，LightGBM更具有优势，分类结果更准确，可见，利用LightGBM算法可以有效地辅助医疗人员对AD进行早期诊断。
Abstract:
 The study aims to make better use of computer technology to analyze brain changes in patients with Alzheimer’s disease (AD), and to assist the diagnosis of AD. Herein the structural magnetic resonance images of 116 patients with AD, 116 ones with mild cognitive impairment and 117 normal controls from AD neuroimaging initiative database are pre-processed with spm software and then are investigated by correlation analysis to obtain abnormal brain regions. Subsequently, IBASPM software is used to extract the volume of brain lesion as a feature sample. Finally, LightGBM algorithm is used to classify the feature vectors, and the obtained results are compared with the results of support vector machine and XGBoost algorithms. Experimental results reveal that the accuracy rate of LightGBM algorithm to classify the volume of brain lesion reaches 83%. Among the 3 algorithms discussed in this study, namely LightGBM, support vector machine and XGBoost, LightGBM algorithm has the highest accuracy rate in classification. Therefore, it is effective for the paramedical staffs to perform an early diagnosis of AD using LightGBM algorithm.

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[1]周文,王瑜,肖红兵,等. 基于KPCA算法的阿尔茨海默症辅助诊断[J].中国医学物理学杂志,2018,35(4):404.[doi:DOI:10.3969/j.issn.1005-202X.2018.04.007]
　ZHOU Wen,WANG Yu,XIAO Hongbing,et al. Assisted diagnosis of Alzheimer’s disease based on KPCA algorithm[J].Chinese Journal of Medical Physics,2018,35(4):404.[doi:DOI:10.3969/j.issn.1005-202X.2018.04.007]
[2]李长胜,王瑜,肖洪兵,等.KPCA和Adaboost算法在阿尔茨海默症功能磁共振影像分类中的应用[J].中国医学物理学杂志,2019,36(7):784.[doi:DOI:10.3969/j.issn.1005-202X.2019.07.008]
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备注/Memo

备注/Memo:
 【收稿日期】2018-11-02
【基金项目】国家自然科学基金（61671028）；国家重大科技研发子课题（ZLJC6 03-5-1）；北京市自然科学基金（4162018）；北京工商大学两科培育基金（19008001270）
【作者简介】周文，硕士研究生，研究方向：医学图像处理、模式识别，E-mail: wenzhoumail@163.com
【通信作者】王瑜，博士，副教授，硕士生导师，研究方向：医学图像处理、模式识别，E-mail: wangyu@btbu.edu.cn
更新日期/Last Update: 2019-04-23