中文版 | English
Title

Nested segmentation and multi-level classification of diabetic foot ulcer based on mask R-CNN

Author
Corresponding AuthorHou, Muzhou; Zhou, Qiuhong; Zhang, Jianglin
Publication Years
2022-11-01
DOI
Source Title
ISSN
1380-7501
EISSN
1573-7721
Abstract
A diabetic foot ulcer(DFU) is a common chronic complication of diabetes because of the dysfunction of islets or receptors of insulin, and it has a high disability and mortality rate. Measuring diabetic foot ulcers is also one of the popular application areas where computer vision combines with deep learning techniques. However, some remaining defects in these studies prevent them from accurately visualizing the wound of different severity. Based on this, we used a multi-classification model to mark the wounds into five grades according to the Wagner diabetic foot grading method. It segmented the different grades in each different level wound using colorfully nested ring shapes to reflect the gradual change of wound grades. We collected 1426 DFU images, of which 967 had nested labels and 459 were single-level labels, with images marked with colored rings to show different degrees of wounds. And then, we constructed a deep learning model of diabetes foot ulcer wounds for semantic segmentation based on Mask Region-based convolutional neural networks (Mask R-CNN), and obtain different levels of diabetes nested segmentation results to reflect the different severity in one wound. Finally, we test and evaluate the performance data of the model. Compared with the state-of- the-art results concerning segmentation and classification and diagnosis of diabetic foot wounds, our model has achieved better performance data (specificity = 99.50%, sensitivity = 70.62%, precision = 84.56%, Mean Average Precision = 85.70%). It shows the effectiveness of our nested segmentation and multi-level classification method. It provides some suggestions and directions for the subsequent evaluation and diagnosis and treatment of diabetic foot ulcers.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Corresponding
Funding Project
Hunan Province Natural Science Foundation[2022JJ30673] ; Scientific Research Fund of Hunan Provincial Education Department[20C0402] ; Hunan First Normal University[XYS16N03] ; Projects of the National Natural Science Foundation of China[82073018] ; Shenzhen Science and Technology Innovation Commission (Natural Science Foundation of Shenzhen)[JCYJ20210324113001005] ; Management Research Fund of Xiangya Hospital of Central South University[2021GL11]
WOS Research Area
Computer Science ; Engineering
WOS Subject
Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS Accession No
WOS:000884646000001
Publisher
ESI Research Field
COMPUTER SCIENCE
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:1
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/412173
DepartmentShenzhen People's Hospital
Affiliation
1.Cent South Univ, Sch Math & Stat, Changsha 410083, Peoples R China
2.Cent South Univ, Dept Dermatol, Xiangya Hosp, Changsha 410008, Peoples R China
3.Hunan First Normal Univ, Sci & Engn Sch, Changsha 410205, Peoples R China
4.Cent South Univ, Teaching & Res Sect Clin Nursing, Xiangya Hosp, Changsha 410008, Peoples R China
5.Southern Univ Sci & Technol, Jinan Univ, Shenzhen Peoples Hosp, Dept Detmatol,Affiliated Hosp 1,Clin Med Coll 2, Shenzhen 518020, Guangdong, Peoples R China
Corresponding Author AffilicationShenzhen People's Hospital
Recommended Citation
GB/T 7714
Cao, Cong,Qiu, Yue,Wang, Zheng,et al. Nested segmentation and multi-level classification of diabetic foot ulcer based on mask R-CNN[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2022.
APA
Cao, Cong.,Qiu, Yue.,Wang, Zheng.,Ou, Jiarui.,Wang, Jiaoju.,...&Zhang, Jianglin.(2022).Nested segmentation and multi-level classification of diabetic foot ulcer based on mask R-CNN.MULTIMEDIA TOOLS AND APPLICATIONS.
MLA
Cao, Cong,et al."Nested segmentation and multi-level classification of diabetic foot ulcer based on mask R-CNN".MULTIMEDIA TOOLS AND APPLICATIONS (2022).
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