A comprehensive review of methods based on deep learning for diabetes-related foot ulcers

Zhang, Jianglin1; Qiu, Yue2; Peng, Li3; Zhou, Qiuhong4; Wang, Zheng3; Qi, Min5

2022-08-08

10.3389/fendo.2022.945020

Frontiers in Endocrinology   Impact Factor & Quartile

1664-2392

BackgroundDiabetes mellitus (DM) is a chronic disease with hyperglycemia. If not treated in time, it may lead to lower limb amputation. At the initial stage, the detection of diabetes-related foot ulcer (DFU) is very difficult. Deep learning has demonstrated state-of-the-art performance in various fields and has been used to analyze images of DFUs. ObjectiveThis article reviewed current applications of deep learning to the early detection of DFU to avoid limb amputation or infection. MethodsRelevant literature on deep learning models, including in the classification, object detection, and semantic segmentation for images of DFU, published during the past 10 years, were analyzed. ResultsCurrently, the primary uses of deep learning in early DFU detection are related to different algorithms. For classification tasks, improved classification models were all based on convolutional neural networks (CNNs). The model with parallel convolutional layers based on GoogLeNet and the ensemble model outperformed the other models in classification accuracy. For object detection tasks, the models were based on architectures such as faster R-CNN, You-Only-Look-Once (YOLO) v3, YOLO v5, or EfficientDet. The refinements on YOLO v3 models achieved an accuracy of 91.95% and the model with an adaptive faster R-CNN architecture achieved a mean average precision (mAP) of 91.4%, which outperformed the other models. For semantic segmentation tasks, the models were based on architectures such as fully convolutional networks (FCNs), U-Net, V-Net, or SegNet. The model with U-Net outperformed the other models with an accuracy of 94.96%. Taking segmentation tasks as an example, the models were based on architectures such as mask R-CNN. The model with mask R-CNN obtained a precision value of 0.8632 and a mAP of 0.5084. ConclusionAlthough current research is promising in the ability of deep learning to improve a patient's quality of life, further research is required to better understand the mechanisms of deep learning for DFUs.

diabetic foot ulcer medical image deep learning classification object detection semantic segmentation

SCI

English

First

[82073018] ; [82073019] ; [JCYJ20210324114212035] ; [2022JJ30189] ; [HNJG-2021-1120]

Endocrinology & Metabolism

Endocrinology & Metabolism

WOS:000843341700001

FRONTIERS MEDIA SA

Web of Science

1.Southern Univ Sci & Technol, Jinan Univ, Shenzhen Peoples Hosp, Affiliated Hosp 1,Dept Dermatol,Clin Med Coll 2, Shenzhen, Peoples R China
2.Cent South Univ, Dermatol Dept Xiangya Hosp, Changsha, Peoples R China
3.Hunan First Normal Univ, Sch Comp Sci, Changsha, Peoples R China
4.Cent South Univ, Teaching & Res Sect Clin Nursing, Xiangya Hosp, Changsha, Peoples R China
5.Cent South Univ, Xiangya Hosp, Dept Plast Surg, Changsha, Peoples R China

Zhang, Jianglin,Qiu, Yue,Peng, Li,et al. A comprehensive review of methods based on deep learning for diabetes-related foot ulcers[J]. Frontiers in Endocrinology,2022,13.

Zhang, Jianglin,Qiu, Yue,Peng, Li,Zhou, Qiuhong,Wang, Zheng,&Qi, Min.(2022).A comprehensive review of methods based on deep learning for diabetes-related foot ulcers.Frontiers in Endocrinology,13.

Zhang, Jianglin,et al."A comprehensive review of methods based on deep learning for diabetes-related foot ulcers".Frontiers in Endocrinology 13(2022).