## 2 Related Work
### Segmentation
\begin{table}
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline
**Reference** & **Application/ Task** & **Approach** & **Data** & **Metric** & **TL\({}^{*}\)** \\ \hline [PERSON] et al., 2021 [35] & Melanoma (Skin) & Pre-trained MobileNet2 to extract patch-wise embeddings and scale-aware labeling & ISC 2020 dataset with soft labeling & Acc, AUC, F1-score, sensitivity, specificity & ✓ \\ \hline [PERSON] et al., 2021 [26] & Melanocytic lesions (skin) & Encoded multi-scale features with transformer & MYATH-Dx dataset with transformers & Acc, AUC, F1-score, sensitivity, specificity & ✗ \\ \hline [PERSON] et al., 2015 [62] & Wound segmentation, infection, detection, healing prediction & Encoder-decoder of ConvNet, SVM, Gaussian process & NYU database & Pixel Acc, mean IoU & ✗ \\ \hline [PERSON] et al., 2017 [64] & Wound segmentation & FCN for diabetic foot ulcer segmentation, two-tier transfer learning with ImageNet and Pascal VOC dataset & Medetec medical image database, approximately a few hundred round images & IoU, precision, recall & ✓ \\ \hline [PERSON] et al., 2018 [63] & Foot ulcer **segmentation** & CNN, color data augmentation & Lancashire Teaching Hospitals & DSC, matthews correlation coefficient, specificity, sensitivity & ✗ \\ \hline [PERSON] et al., 2018 [65] & Pressure ulcer **segmentation** & 3D CNN, RoI, preselected Gaussian kernel 3D hue-saturation-intensity color space images & 193 test color pressure ulcer images, granulation necrotic eschar, and stough tissue images & AUC, DSC, percentage area & ✗ \\ \hline [PERSON] et al., 2019 [66] & Wound **segmentation**, **classification**, baseline & Various color feature spaces, texture spaces, extracted through scale-invariant feature transform, non-negative feature reduction, various generalized linear model, random forest) & Medetec wound database with 341 RGB pressure ulcer images, 36 pressure ulcer images from National Pressure Uicer Advisory Panel website & Acc, precision, sensitivity & ✗ \\ \hline \end{tabular}
\end{table} 
Table 1: Conf. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Application/ Task** & **Approach** & **Data** & **Metric** & **TL\({}^{*}\)** \\ \hline [PERSON] et al., 2019 [67] & Wound **segmentation** & SegNet, LinkNet, U-Net, and U-Net with the VCG16 encoder pre-trained on ImageNet & dataset of pressure ulcers, diabetic foot ulcers, and venous leg ulcers data & Acc, AUC, sensitivity, specificity & ✓ \\ \hline [PERSON] et al., 2023 [68] & Wound **segmentation** & WSBNet with global-local architecture & WoundSeg dataset with 8 types (diabetic, pressure, trauma, venous, surgical, arterial, celluluits and others) & IoU, DSC & ✓ \\ \hline [PERSON] et al., 2023 [69] & Wound **segmentation** & FANNet with a edge feature augment module and a spatial relationship feature augment module, FANet & MSC dataset with 5032 skin wound images and FUSeg dataset with 1010 images & IoU, sensitivity, specificity & ✗ \\ \hline [PERSON] et al., 2020 [57] & Pressure ulcer **segmentation** & Mask R-CNN with mesh rasterization, matching block and measurement block & 210 photographs of pressure injuries including 100 images from DSC, RMSE, mean absolute error & ✓ \\ \hline [PERSON] et al., 2023 [70] & Pressure ulcer **segmentation** & Mask R-CNN with measurement block & 969 pressure injury images from E\&A\&