Title: Diagnosis of Parkinson's disease based on feature fusion on T2 MRI images
Abstract: International Journal of Intelligent SystemsVolume 37, Issue 12 p. 11362-11381 RESEARCH ARTICLE Diagnosis of Parkinson's disease based on feature fusion on T2 MRI images Xinchun Cui, Xinchun Cui School of Computer Science, Qufu Normal University Rizhao, Rizhao, China Department of Public Education, University of Health and Rehabilitation Sciences, Qingdao, ChinaSearch for more papers by this authorYubang Xu, Yubang Xu orcid.org/0000-0002-1430-0747 School of Computer Science, Qufu Normal University Rizhao, Rizhao, ChinaSearch for more papers by this authorYue Lou, Yue Lou Department of Neurology, Zhejiang Hospital, Hangzhou, ChinaSearch for more papers by this authorQinghua Sheng, Qinghua Sheng Department of Pharmacy, Rizhao Central Hospital, Rizhao, ChinaSearch for more papers by this authorMiao Cai, Miao Cai Department of Neurology, Zhejiang Hospital, Hangzhou, ChinaSearch for more papers by this authorLiying Zhuang, Liying Zhuang Department of Neurology, Zhejiang Hospital, Hangzhou, ChinaSearch for more papers by this authorGang Sheng, Gang Sheng School of Information Engineering, Yancheng Teachers University, Yancheng, ChinaSearch for more papers by this authorJiahu Yang, Jiahu Yang Department of Neurology, Zhejiang Hospital, Hangzhou, ChinaSearch for more papers by this authorJinxing Liu, Jinxing Liu School of Computer Science, Qufu Normal University Rizhao, Rizhao, ChinaSearch for more papers by this authorYue Feng, Yue Feng Department of Radiology, Zhejiang Hospital, Hangzhou, ChinaSearch for more papers by this authorXiaoli Liu, Corresponding Author Xiaoli Liu [email protected] orcid.org/0000-0003-4810-1815 Department of Neurology, Zhejiang Hospital, Hangzhou, China Correspondence Xiaoli Liu, Department of Neurology, Zhejiang Hospital, 310013 Hangzhou, China. Email: [email protected] for more papers by this author Xinchun Cui, Xinchun Cui School of Computer Science, Qufu Normal University Rizhao, Rizhao, China Department of Public Education, University of Health and Rehabilitation Sciences, Qingdao, ChinaSearch for more papers by this authorYubang Xu, Yubang Xu orcid.org/0000-0002-1430-0747 School of Computer Science, Qufu Normal University Rizhao, Rizhao, ChinaSearch for more papers by this authorYue Lou, Yue Lou Department of Neurology, Zhejiang Hospital, Hangzhou, ChinaSearch for more papers by this authorQinghua Sheng, Qinghua Sheng Department of Pharmacy, Rizhao Central Hospital, Rizhao, ChinaSearch for more papers by this authorMiao Cai, Miao Cai Department of Neurology, Zhejiang Hospital, Hangzhou, ChinaSearch for more papers by this authorLiying Zhuang, Liying Zhuang Department of Neurology, Zhejiang Hospital, Hangzhou, ChinaSearch for more papers by this authorGang Sheng, Gang Sheng School of Information Engineering, Yancheng Teachers University, Yancheng, ChinaSearch for more papers by this authorJiahu Yang, Jiahu Yang Department of Neurology, Zhejiang Hospital, Hangzhou, ChinaSearch for more papers by this authorJinxing Liu, Jinxing Liu School of Computer Science, Qufu Normal University Rizhao, Rizhao, ChinaSearch for more papers by this authorYue Feng, Yue Feng Department of Radiology, Zhejiang Hospital, Hangzhou, ChinaSearch for more papers by this authorXiaoli Liu, Corresponding Author Xiaoli Liu [email protected] orcid.org/0000-0003-4810-1815 Department of Neurology, Zhejiang Hospital, Hangzhou, China Correspondence Xiaoli Liu, Department of Neurology, Zhejiang Hospital, 310013 Hangzhou, China. Email: [email protected] for more papers by this author First published: 02 September 2022 https://doi.org/10.1002/int.23046 Yue Feng and Xiaoli Liu are equally contributing authors. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Deep-learning methods (especially convolutional neural networks) using magnetic resonance imaging (MRI) data have been successfully applied to computer-aided diagnosis of Parkinson's Disease (PD). Early detection and prior care may help patients improve their quality of life, although this neurodegenerative disease has no known cure. In this study, we propose a FResnet18 model to classify MRI images of PD and Health Control (HC) by fusing image texture features with deep features. First, Local Binary Pattern and Gray-Level Co-occurrence Matrix are used to extract the handcrafted features. Second, the modified ResNet18 network is used to extract deep features. Finally, the fused features are classified by Support Vector Machine. The classification accuracy rate for MRI images reaches 98.66%, and the findings demonstrate that the model can successfully differentiate between PD and HC. The suggested FResnet18 provides greater performance compared with existing approaches, and it is shown through extensive experimental findings on the Parkinson's Disease Progression Markers Initiative data set that feature fusion may improve classification performance. Open Research DATA AVAILABILITY STATEMENT Research data are not shared. Volume37, Issue12December 2022Pages 11362-11381 RelatedInformation
Publication Year: 2022
Publication Date: 2022-09-02
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 5
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