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Found 10 papers, 2 papers with code
Quantifying the Effect of Image Similarity on Diabetic Foot Ulcer Classification
This research conducts an investigation on the effect of visually similar images within a publicly available diabetic foot ulcer dataset when training deep learning classification networks.
Diabetic Foot Ulcer Grand Challenge 2022 Summary
The Diabetic Foot Ulcer Challenge 2022 focused on the task of diabetic foot ulcer segmentation, based on the work completed in previous DFU challenges.
Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation
This paper demonstrates that image processing using refined contour as ground truth can provide better agreement with machine predicted results.
Development of Diabetic Foot Ulcer Datasets: An Overview
This paper provides conceptual foundation and procedures used in the development of diabetic foot ulcer datasets over the past decade, with a timeline to demonstrate progress.
Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary
Diabetic foot ulcer classification systems use the presence of wound infection (bacteria present within the wound) and ischaemia (restricted blood supply) as vital clinical indicators for treatment and prediction of wound healing.
A Cloud-based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers
This research proposes a mobile and cloud-based framework for the automatic detection of diabetic foot ulcers and conducts an investigation of its performance.
Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation
DFUC2020 provided participants with a comprehensive dataset consisting of 2, 000 images for training and 2, 000 images for testing.
DFUC2020: Analysis Towards Diabetic Foot Ulcer Detection
Every 20 seconds, a limb is amputated somewhere in the world due to diabetes.
DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification
In this paper, we have proposed the use of traditional computer vision features for detecting foot ulcers among diabetic patients, which represent a cost-effective, remote and convenient healthcare solution.
Fully Convolutional Networks for Diabetic Foot Ulcer Segmentation
Using 5-fold cross-validation, the proposed two-tier transfer learning FCN Models achieve a Dice Similarity Coefficient of 0. 794 ($\pm$0. 104) for ulcer region, 0. 851 ($\pm$0. 148) for surrounding skin region, and 0. 899 ($\pm$0. 072) for the combination of both regions.
