|
Authors/ Citation
|
Specific Machine
Learning problem
|
Summary of Approach
|
No. of target classes
|
Best performance results
|
|
Wound Ischaemia and Infection Recognition using deep neural network models
|
| Goyal et al. 2020 [12]
|
DFU wound Ischaemia and Infection Recognition |
Ensemble CNN |
2 classes (Ischaemia: Yes/No; Infection: Yes/No) |
accuracy (Ischaemia: 90%, Infection: 73%) |
| Yap et al. 2021 [16]
|
DFU wound Ischaemia and Infection Recognition |
VGG16, ResNet101, InceptionV3, DenseNet121, EfficientNet |
4 classes (both Infection and Ischaemia, Infection, Ischaemia, None) |
EfficientNet B0: macro-average Precision, Recall and F1-Score of 0.57, 0.62 and 0.55 |
| Al-Garaawi et al. 2022 [26]
|
DFU wound Ischaemia and Infection Recognition |
CNN-based DFU classification method |
Part A: 2 classes (healthy skin and DFU); Part B: 2 classes (Ischaemia: Yes/No; Infection: Yes/No) |
Ischaemia: 0.995% (AUC), 0.990% (F-measure) Infection: 0.820% (AUC), 0.744% (F-measure) |
|
Wound Infection Recognition using deep neural network models
|
| Wang et al. 2015 [23]
|
wound segmentation and infection detection |
deep neural network, SVM |
2 classes (infection and no infection) |
infection classification accuracy 95.6% |
| Nejati et al. 2018 [24]
|
classification of 7 tissue types including infection |
deep neural network, SVM |
7 classes (Necrotic, Healthy Gran, Slough, Infected, Unhealthy Gran, Hyper Gran, Epithelialization) |
tissue classification accuracy 86.4% |
|
Wound Infection Recognition using traditional machine learning techniques
|
| Hsu et al. 2017 [21]
|
detection of 4 tissue types including infection |
clustering method and classification using SVM |
4 classes (Swelling, Blood Region, Infected, Tissue Necrosis) |
detection accuracy of 95.23% |
| Hsu et al. 2019 [22]
|
wound segmentation and infection detection |
robust image segmentation, classification using SVM |
4 classes (Swelling, Granulation, Infection, Tissue Necrosis) |
tissue classification accuracy 83.58 |
| (b) Statistics of Different Versions of the DFUC2021 Dataset |
|
|
|
|
|
Authors/ Citation
|
Specific Machine
Learning problem
|
No. of target classes
|
Statistics of dataset
|
|
| Goyal et al. 2020 [12]
|
DFUC2021 dataset classification |
2 classes (Ischaemia: Yes, No; Infection: Yes, No) |
Ischaemia: (Yes, 235; No, 1431) augmented to (Yes, 4935; No, 4935); Infection: (Yes, 982; No, 684) augmented to (Yes, 2946; No, 2946) |
|
| Yap et al. 2021 [16]
|
DFUC2021 dataset classification |
4 classes (both Infection and Ischaemia, Infection, Ischaemia, None) |
both Infection and Ischaemia: 621; Infection: 2555; Ischaemia: 277; none of them: 2552 |
|
| Al-Garaawi et al. 2022 [26]
|
DFU dataset classification and DFUC2021 dataset classification |
Part A: 2 classes (healthy skin and DFU); Part B: 2 classes (Ischaemia: Yes, No; Infection: Yes, No) |
Part A: 641 healthy, 1038 Ulcer Part B: Ischaemia: augmented (Yes, 4935; No, 4935); Infection: augmented (Yes, 2946; No, 2946) |
|
| Goyal et al. 2018 [25]
|
DFU dataset classification |
2 classes (healthy skin and DFU) |
641 healthy, 1038 DFU |
|
