## 3 Results  
This section presents the performance results of the VGG16, VGG19 and MobileNetV2 models on the validation dataset. In Table 4 it is possible to observe a summary of the results of the performance metrics for each model.  
The performance metrics calculated are fundamental to evaluating the effectiveness of the models and determining their clinical applicability. Through this evaluation, it is possible to identify not only the overall performance but also the areas in which each model can be improved, contributing to the continuous improvement of the automatic screening system for patients with DFU. Table 5 presents the initial performance metrics for the original dataset in the \"raw\" column, highlighting the early modest results. The remaining columns show the F1-score for each augmentation technique applied individually. This clearly demonstrates the impact of the data augmentation process, which not only expanded the dataset but also increased its diversity.  
To further assess model behaviour, Table 6 presents the confusion matrices for the MobileNetV2, VGG16 and VGG19 models, providing a detailed overview of the performance of the model.  
The results of the confusion matrices offer a deeper insight into the performance of each model, complementing the metrics presented above.  
The evaluation with Grad-CAM then comes in as a qualitative analysis, helping to visually analyse the previously obtained results. Figure 8 shows the produced images  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Skin Tone** & **TP** & **FN** & **FP** & **TN** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Very Light & 56 & 2 & 1 & 57 & 97\% & 98\% & 97\% & 97\% \\ Light & 48 & 7 & 11 & 50 & 84\% & 81\% & 87\% & 84\% \\ Intermediate & 88 & 8 & 7 & 70 & 91\% & 93\% & 92\% & 92\% \\ Tan & 139 & 3 & 3 & 209 & 98\% & 98\% & 98\% & 98\% \\ Brown & 417 & 8 & 7 & 469 & 98\% & 98\% & 98\% & 98\% \\ Dark & 260 & 3 & 2 & 299 & 99\% & 99\% & 99\% & 99\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of the results for the VGG19 model grouped by ITA skin tone.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Model** & **TP** & **FN** & **FP** & **TN** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline MobileNetV2 & 769 & 270 & 169 & 1016 & 80\% & 82\% & 74\% & 78\% \\ VGG16 & 966 & 73 & 84 & 1101 & 93\% & 92\% & 93\% & 92\% \\
**VGG19** & **1008** & **31** & **31** & **1154** & **97\%** & **97\%** & **97\%** & **97\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance metrics obtained using the balanced and augmented dataset.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **Raw** & **Rot./Flip.** & **Trans.** & **Scaling** & **Shear** & **Bright.** \\ \hline \multirow{3}{*}{MobileNetV2} & \multirow{3}{*}{67\%} & 72\% & 69\% & 72\% & 69\% & 71\% \\  & & (+5\%) & (+2\%) & (+5\%) & (+2\%) & (+4\%) \\ \multirow{2}{*}{VGG16} & \multirow{2}{*}{74\%} & 79\% & 77\% & 77\% & 78\% & 80\% \\  & & (+5\%) & (+3\%) & (+3\%) & (+4\%) & (+6\%) \\ \multirow{2}{*}{VGG19} & \multirow{2}{*}{70\%} & 74\% & 73\% & 74\% & 72\% & 74\% \\  & & (+4\%) & (+3\%) & (+4\%) & (+2\%) & (+4\%) \\ \hline \hline \end{tabular}
\end{table}
Table 5: F1-score obtained by training the model with the original dataset (raw) and by applying each augmentation technique individually. The improvement difference is presented in parenthesis.  
using this tool on VGG16, VGG19 and MobileNetV2, respectively. The heatmaps allow to visually explain which regions of an input image the CNN model focuses on when making a particular classification decision.