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

Cao, Cong1; Qiu, Yue2; Wang, Zheng1,3; Ou, Jiarui2; Wang, Jiaoju1; Hounye, Alphonse Houssou1; Hou, Muzhou1; Zhou, Qiuhong4; Zhang, Jianglin5

2022-11-01

10.1007/s11042-022-14101-6

MULTIMEDIA TOOLS AND APPLICATIONS   Impact Factor & Quartile

1380-7501

1573-7721

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.

Diabetic foot ulcer (DFU) Nested-structure Wound Multi-level classification Mask Region based convolutional neural networks (mask R-CNN)

SCI

English

Corresponding

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]

Computer Science ; Engineering

Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic

WOS:000884646000001

SPRINGER

COMPUTER SCIENCE

Web of Science

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

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.

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.

Cao, Cong,et al."Nested segmentation and multi-level classification of diabetic foot ulcer based on mask R-CNN".MULTIMEDIA TOOLS AND APPLICATIONS (2022).