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Application of innovative image processing methods and AdaBound-SE-DenseNet to optimize the diagnosis performance of meningiomas and gliomas
Huang Z(黄钲)1,2,3; Zhao YW(赵忆文)1,2; Li, Xin4; Zhao XG(赵新刚)1,2; Liu YH(刘云辉)5,6; Song GL(宋国立)1,2,6; Luo Y(罗阳)1,2
Department机器人学研究室
Corresponding AuthorSong, Guoli(songgl@sia.cn) ; Luo, Yang(luoyang@sia.cn)
Source PublicationBiomedical Signal Processing and Control
ISSN1746-8094
2020
Volume59Pages:1-11
Indexed BySCI ; EI
EI Accession number20201008270298
WOS IDWOS:000528276200034
Contribution Rank1
Funding OrganizationNational Key R&D Program of China [grant number 2017YFB1302802] ; the 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
KeywordBrain tumors Brain region extraction Iterative gamma correction AdaBound-SE-DenseNet
Abstract

With the development of artificial intelligence, numerous computer-aided diagnosis systems (CADSs) have been proposed to diagnosis meningiomas and gliomas automatically. However, most current systems not only ignore the large intra-class variances among original brain images, but also employ small databases with expensive labeling costs; as a result, the performances of most CADSs are well below expectations. To optimize the diagnosis performance of meningiomas and gliomas, novel image processing methods, including a novel multi-directional brain region extraction (MDBRE) method and an iterative gamma correction based on two peaks (TPGC), are proposed to narrow the intra-class variances, and a pre-trained AdaBound-SE-DenseNet (AD-SE-DenseNet) is presented to avoid over-fitting. First, innovative image processing methods, including a novel MDBER and a novel TPGC, are applied to remove the disturbances of skulls and brightness variances. Then, data augmentation technologies are applied to produce a larger database and a pretrained AD-SE-DenseNet is introduced to train the classifier. The experimental results indicate that the accuracy of this system can reach 96.87%. Implementing the innovative image processing methods and AD-SE-DenseNet can lead to a nearly 8% and 1.7% accuracy improvement, respectively.

Language英语
WOS SubjectEngineering, Biomedical
WOS KeywordBRAIN ; CLASSIFICATION ; TRANSFORM
WOS Research AreaEngineering
Funding ProjectNational Key R&D Program of China[2017YFB1302802] ; National Natural Science Foundation of China[61703394] ; Special Fund for High-level Talents
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/26446
Collection机器人学研究室
Corresponding AuthorSong GL(宋国立); Luo Y(罗阳)
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.The First Hospital of Qiqihaer City, Qiqihaer 161005, China
5.Shengjing Hospital of China Medical University, Shenyang 110011, China
6.Liaoning Medical Surgery and Rehabilitation Robot Engineering Research Center, Shenyang 110134, China
Recommended Citation
GB/T 7714
Huang Z,Zhao YW,Li, Xin,et al. Application of innovative image processing methods and AdaBound-SE-DenseNet to optimize the diagnosis performance of meningiomas and gliomas[J]. Biomedical Signal Processing and Control,2020,59:1-11.
APA Huang Z.,Zhao YW.,Li, Xin.,Zhao XG.,Liu YH.,...&Luo Y.(2020).Application of innovative image processing methods and AdaBound-SE-DenseNet to optimize the diagnosis performance of meningiomas and gliomas.Biomedical Signal Processing and Control,59,1-11.
MLA Huang Z,et al."Application of innovative image processing methods and AdaBound-SE-DenseNet to optimize the diagnosis performance of meningiomas and gliomas".Biomedical Signal Processing and Control 59(2020):1-11.
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