## 3 Results
### Classification
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Study Aim** & **Data Type** & **Model Used** & **Code Availability** & **Hyperparameter** & **Training Protocols** & **Evaluation Metrics** \\ \hline
[13] & 2020 & Classify DFUs and healthy skin & Image & DFU\_QUTNet & Not & Batch size: 32, learning rate: 0.5, dropout rate: 0.5, number of layers: 58 & Training: & 1. DFU\_QUTNet: 94.5\% & 2. AlexNet: 89.1\% \\ \hline
[30] & 2020 & Classification and localization of ischemia in DFUs & Image & CNN, YOLOv2 & Not provided & Learning rate: 0.001, epochs: 40 & \
\begin{tabular}{c} Training: \\ 50\%, testing: \\ 50\% \\ \end{tabular} &
\begin{tabular}{c} Accuracy: \\ 1. ResNet50: \\ 4. GoogleNet: 93.1\% \\ \end{tabular} \\ \hline
[15] & 2023 & Classify ischemia and infection in DFUs & Image & ResNet50 & Not provided & \begin{tabular}{c} Batch size: 32, learning rate: 0.001, \\ momentum: 0.8, epochs: 30 \\ \end{tabular} & \
\begin{tabular}{c} Training: \\ 80\%, validation: \\ 10\%, testing: 10\% \\ \end{tabular} &
\begin{tabular}{c} Accuracy: \\ 1. ResNet50: \\ 4. GoogleNet: 93.1\% \\ \end{tabular} \\ \hline
[14] & 2023 & Classify ischemia & Image & DFU-SIAM + KNN & Not provided & \
\begin{tabular}{c} Batch size: 8, learning rate: 10 \% \%, validation (K = 5) \\ \end{tabular} &
\begin{tabular}{c} K-fold \\ validation \\ (K = 5) \\ \end{tabular} \\ \hline
[14] & 2023 & Classify ischemia and infection in DFUs & Image & EfficientNet & Not provided & \
\begin{tabular}{c} Batch size, \\ learning rate \\ \end{tabular} &
\begin{tabular}{c} Accuracy: \\ 1. EfficientNet: \\ Infection: 97\% \\ \end{tabular} \\ \hline
[14] & 2023 & Classify ischemia and infection in DFUs & Image & EfficientNet & Not provided & \begin{tabular}{c} Batch size, \\ learning rate \\ \end{tabular} & \
\begin{tabular}{c} Training: \\ 70\%, validation: \\ 15\%, testing: 15\%, validation, \\ 5-fold cross-validation, \\ \end{tabular} &
\begin{tabular}{c} Accuracy: \\ 1. EfficientNet: \\ Infection: 97\% \\ \end{tabular} \\ \hline
[14] & 2023 & Classify ischemia and infection in DFUs & Image & EfficientNet & Not provided & \
\begin{tabular}{c} Batch size: 32, learning rate: 0.05, \\ learning rate: 0.5, number of layers: 58 \\ \end{tabular} &
\begin{tabular}{c} Training: \\ 70\%, validation: \\ 20\%, testing: 10\%, testing: 95\% \\ \end{tabular} \\ \hline
[35] & 2023 & Classify abrasion & & & & & 1. Vgg-19 with UNet++: \\
[35] & 2023 & Classify rhesus and healthy skin & Image & \
\begin{tabular}{c} Batch size: 32, learning rate: 0.05, \\ + SVM \\ \end{tabular} &
\begin{tabular}{c} Training: \\ 70\%, validation: \\ 20\%, testing: 90\% \\ \end{tabular} \\ \hline
[35] & 2023 & Classify DFUs and healthy skin & Image & \begin{tabular}{c} VGG-19 with \\ UNet++ \\ \end{tabular} & \
\begin{tabular}{c} Not \\ 10 epochs: \\ \end{tabular} &
\begin{tabular}{c} Training: \\ 70\%, validation: \\ 20\%, testing: 98\% \\ \end{tabular} \\ \hline
[35] & 2023 & Classify rhesus and healthy skin & Image & \
\begin{tabular}{c} VGG-19 with \\ UNet++ \\ \end{tabular} &
\begin{tabular}{c} ResNet50: \\ 1. ResNet50: \\ 4. GoogleNet: 93.1\% \\ \end{tabular} \\ \hline
[30] & 2020 & Classify ischemia and infection in DFUs & Image & \begin{tabular}{c} ResNet50 \\ 1. ResNet50 \\ \end{tabular} & \
\begin{tabular}{c} Batch size: 32, learning rate: 0.001, \\ momentum: 0.8, \\ 10\%, testing: 10\% \\ \end{tabular} &
\begin{tabular}{c} Training: \\ 80\%, validation: \\ 4. GoogleNet: 93.1\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of AI-based classification models for DFUs.