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Found 4 papers, 1 papers with code
DFUC2020: Analysis Towards Diabetic Foot Ulcer Detection
Every 20 seconds, a limb is amputated somewhere in the world due to diabetes.
Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniques
We found that our proposed Ensemble CNN deep learning algorithms performed better for both classification tasks as compared to handcrafted machine learning algorithms, with 90% accuracy in ischaemia classification and 73% in infection classification.
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.
