## 4 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **Value** & **Architecture** & **Loss** & **Image Type** \\ \hline \multirow{4}{*}{MCC} & **0.9252** & **[PERSON]** & **Dice** & **Visible** \\  & 0.9248 & Choi & Focal Tversky & Visible \\  & 0.9231 & Akhloufi & Focal Tversky & Visible \\  & 0.9151 & Choi & Focal Tversky & FIRe-GAN \\  & 0.9140 & Akhloufi & Focal Tversky & NIR \\ \hline \multirow{4}{*}{F1} & **0.9323** & **[PERSON]** & **Dice** & **Visible** \\  & 0.9274 & Akhloufi & Focal Tversky & Visible \\ \cline{1-1}  & 0.9265 & Choi & Focal Tversky & Visible \\ \cline{1-1}  & 0.9176 & Choi & Focal Tversky & FIRe-GAN \\ \cline{1-1}  & 0.9165 & Akhloufi & Focal Tversky & NIR \\ \hline \multirow{4}{*}{HAF} & **0.9098** & **[PERSON]** & **[PERSON]** & **Visible** \\  & 0.9068 & Choi & Focal Tversky & Visible \\ \cline{1-1}  & 0.8957 & Akhloufi & Focal Tversky & Visible \\ \cline{1-1}  & 0.8904 & Akhloufi & Object & NIR \\ \cline{1-1}  & 0.8892 & Akhloufi & Focal Tversky & NIR \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Top five performing combinations per metric. The best performing combinations per metric are highlighted in bold. Figure 17: Sample segmentation results for all image types and the _[PERSON] + Focal Tversky_ combination. Considering the obtained results, both in terms of performance and robustness, we choose the _[PERSON] + Focal Tversky + visible_ combination as the best one and use it for comparison against the best traditional method identified by [PERSON] et al. [11]. Table 3 shows the results of this comparison. We can observe that the best combination that we identify clearly outperforms the best traditional method identified by [PERSON] et al. [11] across all metrics. Figure 18: Spearman correlation matrix for all parameters. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Method** & **Value** \\ \hline \multirow{2}{*}{MCC} & **[PERSON] + Focal Tversky + visible** & **0.92** \\  & [PERSON] et al. [84] & 0.81 \\ \hline \multirow{2}{*}{F1} & **[PERSON] + Focal Tversky + visible** & **0.92** \\  & [PERSON] et al. [84] & 0.82 \\ \hline \multirow{2}{*}{HAF} & **[PERSON] + Focal Tversky + visible** & **0.89** \\  & [PERSON] et al. [84] & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between the best combination and the best traditional method per metric. The best performing method per metric is highlighted in bold. Next, we take this _Akhloufi + Focal Tversky + visible_ and fine-tune its hyperparameters on the training set through five-fold cross-validation. Although the learning rate, optimizer, and batch size proved to be already at optimal values, we were able to halve the training epochs to 50, maintaining the performance reported in Table 3.  
We then incorporate the three different attention modules introduced in Section 3.4 to the _Akhloufi_ architecture as shown in Figures 19-21, and benchmark the _Akhloufi + Focal Tversky + visible_ combination with and without attention modules and the fine-tuned hyperparameters to explore if the inclusion of these modules further improves the segmentation performance. Table 4 presents the results of this comparison. Finally, we can observe that the inclusion of attention modules, in particular of the MECA ones, increases the segmentation performance across all metrics; however, this improvement is too small to be considered significant.