## 5 Discussion  
### Wound Segmentation  
MobileNet-Unet was the selected architecture for the wound segmentation model due to its good performance and advantageous characteristics. It achieved 82.06% \(\pm\) 0.55% mean IoU when tested on the five test folds. MobileNet is a widely used structure with low memory requirements, high processing speed, and fewer parameters than other networks, which can be highly beneficial for this specific type of problem and for the future applications of this work. In terms of the decoder's frameworks, there is no way to distinguish the best network because SegNet had a significantly better score than Unet for ResNet50 but worse for MobileNet, in which there is only a slight variance between both. The reported results demonstrate that the Adam optimizer's best batch size is 32, which is corroborated by [38], who suggested a 32 batch size was a reasonable default value. Commonly, larger batch sizes lead to poor generalization and can take a long to reach the optimal minimum. On the other hand, smaller batch sizes have shown faster convergence because they allow the model to start learning before seeing all data. Nevertheless, the model may never reach the optimal minimum. The presented outcomes on smaller batch sizes agree with several authors [39; 40], which stated that a smaller batch size should be used. In addition, there is a high correlation between the learning rate and the batch size, where larger batch sizes perform better with high learning rates. In the present segmentation model, there was no optimization regarding the learning rate; the default values were 0.001 and 0.01 for Adam and SGD, respectively. Hence, these low learning rates demonstrated their better performance with the experimented small batch sizes. Regarding the optimizer's choice, Adam is already an upgrade of SGD, which was proven for this dataset with the exhibited mean IoU. Regarding the reported results, the MobileNet-Unet architecture achieved its best performance with the Adam optimizer, a 32 batch size, and 50 epochs. Data augmentation aims to improve the generalization of a model by artificially inflating the training dataset size with transformed data, introducing more information for the model to learn. However, the results remained the same with and without augmentation, concluding that the augmentation had no significant effect on the dataset. There are two possible explanations for this occurrence: a large number of data samples and the misrepresenting image transformations. Other combinations of geometrical and color modifications can be tried in the future to see if the model improves beyond its achieved performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Wound** & & & & & & \\
**Type** & **Accuracy\%** & **Precision\%** & **Recall\%** & **F1\%** & **F2\%** & **AUC\%** \\
**Model** & & & & & & \\ \hline CW & \(95.6\pm 1.2\) & \(62.7\pm 7.4\) & \(51.5\pm 5.7\) & \(56.6\pm 5.7\) & \(53.4\pm 6.3\) & \(68.4\pm 6.0\) \\ DW & \(97.6\pm 1.1\) & \(34.6\pm 7.6\) & \(33.8\pm 7.2\) & \(34.1\pm 5.5\) & \(34.0\pm 5.6\) & \(57.9\pm 8.5\) \\ LW & \(87.2\pm 1.9\) & \(61.5\pm 4.5\) & \(73.7\pm 5.3\) & \(67.0\pm 4.0\) & \(70.9\pm 5.0\) & \(80.1\pm 5.7\) \\ Full dataset & \(95.4\pm 2.0\) & \(51.1\pm 7.3\) & \(46.2\pm 6.9\) & \(48.6\pm 5.8\) & \(47.2\pm 6.8\) & \(71.8\pm 3.9\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Final evaluation metrics for the best optimized algorithm regarding F1-score optimization.