SIA OpenIR  > 机器人学研究室
A computer-aided diagnosis system for brain magnetic resonance imaging images using a novel differential feature neural network
Huang Z(黄钲)1,2,3; Xu, Han1,4; Su, Shun1,2,3; Wang, Tianyu5; Luo, Yang1,2; Zhao XG(赵新刚)1,2; Liu YH(刘云会)6,7; Song GL(宋国立)1,2,7; Zhao YW(赵忆文)1,2
Department机器人学研究室
Source PublicationComputers in Biology and Medicine
ISSN0010-4825
2020
Volume121Pages:1-11
Indexed BySCI ; EI
EI Accession number20202108678770
WOS IDWOS:000542187300024
Contribution Rank1
Funding OrganizationNational Key R&D f of China [grant number 2017YFB1302802] ; National Natural Science Foundation of China [grant number 61703394] ; Special Fund for High-level Talents (Shizhen Zhong Team) of the People’s Government of Luzhou-Southwestern Medical University ; National Natural Science Foundation of China [grant number 61821005]
KeywordBrain tumor diagnosis Differential feature neural network Magnetic resonance imaging
Abstract

To improve the performance of brain tumor diagnosis, numerous automatic brain tumor diagnosis systems that use machine learning technologies have been proposed. However, most current systems ignore the structural symmetry of brain magnetic resonance imaging (MRI) images and regard brain tumor diagnosis as a simple pattern recognition task. As a result, the performance of the current systems is not ideal. To improve the performance of the brain tumor screening process, an innovative differential feature map (DFM) block is proposed to magnify tumor regions, and DFM blocks are further combined with squeeze-and-excitation (SE) blocks to form a differential feature neural network (DFNN). First, an automatic image rectification method is applied so that the symmetry axes of brain MRI images are approximately parallel to the perpendicular axis. Moreover, a DFNN is constructed to classify the brain MRI images into two categories: "abnormal" and "normal". The experimental results show that the average accuracy of the proposed system on two databases can reach 99.2% and 98%, and the introduction of the proposed DFM block can improve the average accuracy on these two databases by 1.8% and 1.3%, respectively, which indicates that the proposed DFM block can improve the performance of the brain tumor screening process.

Language英语
WOS SubjectBiology ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology
WOS Research AreaLife Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology
Funding ProjectNational Key RD f of China[2017YFB1302802] ; National Natural Science Foundation of China[61703394] ; National Natural Science Foundation of China[61821005] ; Special Fund for High-level Talents (Shizhen Zhong Team) of the People's Government of LuzhouSouthwestern Medical University
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/26861
Collection机器人学研究室
Corresponding AuthorSong GL(宋国立); Zhao YW(赵忆文)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
4.College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
5.School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, China
6.Shengjing Hospital of China Medical University, Shenyang, CO 110011, China
7.Liaoning Medical Surgery and Rehabilitation Robot Engineering Research Center, Shenyang, CO 110134, China
Recommended Citation
GB/T 7714
Huang Z,Xu, Han,Su, Shun,et al. A computer-aided diagnosis system for brain magnetic resonance imaging images using a novel differential feature neural network[J]. Computers in Biology and Medicine,2020,121:1-11.
APA Huang Z.,Xu, Han.,Su, Shun.,Wang, Tianyu.,Luo, Yang.,...&Zhao YW.(2020).A computer-aided diagnosis system for brain magnetic resonance imaging images using a novel differential feature neural network.Computers in Biology and Medicine,121,1-11.
MLA Huang Z,et al."A computer-aided diagnosis system for brain magnetic resonance imaging images using a novel differential feature neural network".Computers in Biology and Medicine 121(2020):1-11.
Files in This Item: Download All
File Name/Size DocType Version Access License
A computer-aided dia(2943KB)期刊论文出版稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Huang Z(黄钲)]'s Articles
[Xu, Han]'s Articles
[Su, Shun]'s Articles
Baidu academic
Similar articles in Baidu academic
[Huang Z(黄钲)]'s Articles
[Xu, Han]'s Articles
[Su, Shun]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Huang Z(黄钲)]'s Articles
[Xu, Han]'s Articles
[Su, Shun]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: A computer-aided diagnosis system for brain magnetic resonance imaging images using a novel differential feature neural network.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.