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 S(苏顺)1,2,3; Wang TY(王天宇)5![]() ![]() ![]() ![]() ![]() | |
Department | 机器人学研究室 |
Source Publication | Computers in Biology and Medicine
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ISSN | 0010-4825 |
2020 | |
Volume | 121Pages:1-11 |
Indexed By | SCI ; EI |
EI Accession number | 20202108678770 |
WOS ID | WOS:000542187300024 |
Contribution Rank | 1 |
Funding Organization | National 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] |
Keyword | Brain 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 Subject | Biology ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology |
WOS Research Area | Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology |
Funding Project | National 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 |
Identifier | http://ir.sia.cn/handle/173321/26861 |
Collection | 机器人学研究室 |
Corresponding Author | Song GL(宋国立); Zhao YW(赵忆文) |
Affiliation | 1.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 S,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 S.,Wang TY.,Luo Y.,...&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. |
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