## 3 Results
### Detection
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Study Aim** & \begin{tabular}{c} **Data** \\ **Type** \\ \end{tabular} & **Model Used** & \begin{tabular}{c} **Code** \\ **Availability** \\ \end{tabular} & **Hyperparameter** & \begin{tabular}{c} **Training** \\ **Protocols** \\ \end{tabular} & **Evaluation Metrics** \\ \hline \multirow{4}{*}{[56]} & \multirow{4}{*}{2024} & \multirow{4}{*}{\begin{tabular}{c} Segmentation of \\ DFUs \\ \end{tabular} } & \multirow{4}{*}{Image} & \multirow{4}{*}{FUSgeNet} & \multirow{4}{*}{\begin{tabular}{c} https:// \\ github.com/ \\ mriual54 \\ (accessed on \\ 25 \\ 2024) \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Learning rate: \\ 1 x 10\({}^{-4}\), batch size: \\ 2, loss function: Mice \\ and focal loss, \\ epochs: 200 \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Dice Score: \\ 1. x-FUSgeNet 89\% \\ 2. UNet with \\ 3. Stacked UNets: 86\% \\ \end{tabular} } \\ \hline \multirow{4}{*}{[57]} & \multirow{4}{*}{2024} & \multirow{4}{*}{\begin{tabular}{c} DFU segmentation \\ using self-training \\ and mixup \\ augmentation \\ \end{tabular} } & \multirow{4}{*}{Image} & \multirow{4}{*}{\begin{tabular}{c} Attention \\ UNet \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Not \\ provided \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Learning rate: 0.001, \\ batch size: 8, epochs: \\ 750, loss function: \\ \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{c} Training: \\ 70\%, \\ validation: \\ 10\%, \\ testing: 20\%, \\ 5-fold cross- \\ validation \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} Dice Score: \\ 1. DFC 2022: 0.711 \\ 2. FUSge: 0.859 \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table} 
Table 3: \(Cont. predictions. The researchers evaluated this system using the DFUC2020 dataset [32] and validated it externally on the IEEE DataPort Diabetic Foot dataset [64]. Despite challenges such as distracting background objects in the validation set, the system achieved good results. [PERSON] et al. [65] introduced FusionNet, a deep learning framework designed for DFU detection using multi-scale feature fusion and explainable artificial intelligence (XAI). The researchers preprocessed the DFU dataset from Kaggle [36] by applying Gaussian and median filtering, noise removal, and motion blur correction. Data augmentation techniques, such as rotation, flipping, zooming, and shearing, were applied to the training set to enhance diversity. FusionNet's architecture combines three pre-trained CNN models, which are DenseNet201, VGG19, and NASNetMobile. DenseNet201 captures high-dimensional features, VGG19 focuses on fine-grained details, and NASNetMobile offers scalability and efficiency. Transparency is enhanced through integrated XAI algorithms, such as SHapely Adaptive Explanations (SHAP), Gradient-Weighted Class Activation Mapping (Grad-CAM), and Local Interpretable Model-Agnostic Explanations (LIME), which provide interpretable visualizations of predictions. The model achieved exceptional results, outperforming single-model approaches such as VGG19, DenseNet201, and NASNetMobile. [PERSON] et al. [23] introduced a deep learning approach for detecting DFUs using CNNs. The model utilizes the DFUC2021 dataset [34] and employs preprocessing techniques such as scaling, noise removal, and normalization to improve data quality and enhance model generalization. The architecture leverages DenseNet121, which incorporates dense blocks connected by transition layers that down-sample while preserving crucial information. The ReLU activation function adds non-linearity, while global average pooling minimizes spatial dimensions to reduce overfitting. Fully connected layers aggregate the extracted features for final classification into ischemia, infection, and none. The researchers pre-trained the model on the ImageNet dataset, allowing it to use transfer learning and demonstrate higher speed and precision compared to alternatives like EfficientNet-B3.