## 4 Discussion
### Performance Metrics Analysis  
In terms of the VGG16 model, the accuracy achieved was 93%, indicating that the model correctly classified 93% of the DFU images in the validation set. The VGG19 model had a higher accuracy, reaching 97%. This result suggests that VGG19, having a deeper architecture, was able to identify relevant patterns in the images more effectively. Despite being optimised for efficiency and use on mobile devices, as mentioned above, MobileNetV2 obtained an accuracy of 80%. This shows that, despite its lightness, the model was still able to carry out effective classification, albeit with a lower performance compared to the VGG models. These results highlight the effectiveness of the models in correctly identifying ulcer characteristics, with VGG19 standing out as the most robust among them. Accuracy, as a  
Figure 8: Examples of the heatmaps generated using Grad-CAM analysis on the tested CNN models. The overlayed colours represent: [PERSON]/Yellow colors (Warm Colors) indicate regions of high importance or strong activation, meaning that the pixels in these areas heavily contributed to the model’s decision to classify the image as the target class. Blue/Green colors (Cool Colors) indicate regions of low importance or weak activation, with pixels in these areas having little to no influence on the model’s prediction for that specific class. metric, offers an initial view of the models' performance, serving as a starting point for more detailed analyses and the exploration of other performance metrics, such as precision, recall and F1-score, which provide a more comprehensive understanding of the models' behaviour under different conditions. In a diabetic foot ulcer classification scenario, it is essential to minimise FPs, as this can lead to incorrect diagnoses and unnecessary treatment for patients. Therefore, high accuracy indicates that when the model classifies an image as 'abnormal', then there is a high probability that the image actually shows an ulcer. The accuracy results for each model were analysed separately for the 'abnormal' and 'normal' classes for better insight. The MobileNetV2 model obtained an accuracy of 74% (recall) for the 'abnormal' class, which indicates a good probability that the ulcer classifications are correct, while the 79% accuracy (specificity) for the 'normal' class suggests that it tends to incorrectly classify some normal images as abnormal. VGG16 showed a balanced accuracy of 93% for both classes, demonstrating consistent and reliable performance in all classifications. VGG19, on the other hand, stood out with an accuracy of 97% for the 'abnormal' class, showing excellent ability in identifying images with ulcers. A high recall value indicates that the model is identifying most of the positive cases correctly, i.e., there are few false negatives. In this case, a false negative would be for the model to consider an image as normal, but in fact that image has an ulcer on a diabetic foot. In a scenario where a model uses clinical data, such as ulcer detection, the aim is to have a high recall, as this means that the model is identifying the majority of ulcer cases correctly. However, a high recall percentage can come at the expense of an increase in false positives, which can be captured by precision. For the MobileNetV2 model, the recall for the 'abnormal' class was 74%, meaning that the model correctly identified 74% of the images with ulcers. VGG19 obtained the best recall value of 97%. The F1-score can be crucial in clinical applications, such as the classification of diabetic foot ulcers, where both the ability to correctly detect ulcers (recall) and the accuracy of positive predictions (precision) have direct implications for treatment and diagnosis. The MobileNetV2 model obtained an F1-Score of 78%. This result indicates a reasonable balance between precision and recall. VGG16 had an F1-Score of 93%, pointing again to a good compromise between precision and recall. VGG19 obtained an F1-Score of 97%, making it the best performing model.