## 3 Results
### Numerical Results  
To examine the overall model performance, we calculated standard metrics, such as precision, recall, and area under the ROC curve. The numerical results are presented for two versions of the model--with and without squeeze-exciting blocks. The comparison between these two models is shown in Table 1.  
When evaluating on the test set, the Jaccard index [28] and the F1-score were also defined to properly test the trained models (see Table 2). The Jaccard index \(J_{(A,B)}\) and F1-score \(F1_{(A,B)}\) are defined as follows:  
\[J_{(A,B)}=\frac{|A\cap B|}{|A\cup B|}, \tag{2}\]  
\[F1_{(A,B)}=\frac{2\cdot|A\cap B|}{|A|+|B|}, \tag{3}\]  
where \(A\cap B\) is the intersection of sets \(A\) and \(B\), and \(A\cup B\) is the union of sets \(A\) and \(B\). As we are dealing with a multi-class segmentation problem, the aforementioned indicators were calculated in three different variants: micro, macro, and weighted. With the'micro' variant, we calculate metrics globally, i.e., by counting the total true positives, true negatives, false negatives, and false positives. For the'macro' variant, we calculate metrics for each label, and then, we calculate their unweighted mean. This does not take label imbalance into account. In case of the 'weighted' variant, we calculate metrics for each label and find their average, which is weighted by the number of true instances for each label. This alters'macro' to account for label imbalance. Based on the results presented in Table 2, we can see that the squeeze-excitation blocks enhance the overall performance of the model. They also provide a better generalization of the problem, which can be seen by analyzing the loss function across different sets. The learning curves are presented for the model that contains squeeze-excitation blocks since it yields better results (see Figures 4-8). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Without Squeeze-Exciting**} & \multicolumn{3}{c}{**With Squeeze-Exciting**} \\ \hline
**Dataset** & **Train** & **Validation** & **Test** & **Train** & **Validation** & **Test** \\ \hline Accuracy & 0.97 & 0.92 & 0.92 & 0.97 & 0.95 & 0.95 \\ AUC & 0.99 & 0.98 & 0.98 & 0.99 & 0.99 & 0.99 \\ Specificity & 1.0 & 0.99 & 0.97 & 0.99 & 0.99 & 0.95 \\ Sensitivity & 1.0 & 0.99 &  
Accuracy is a percent of correct predictions. It describes the performance of the model among classes. Accuracy is given by the following formula [29]:  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN}, \tag{4}\]  
where _TP_--true positive value, _TN_--true negative value, _FP_--false positive value, _FN_--false negative value (applies to Formulas (4)-(8)). AUC, Area Under the ROC Curve, provides information on how well the model distinguishes between the classes. The desired value is an AUC as close as possible to \(1\). Figure 4: Learning curves—accuracy and AUC among the epochs. Figure 5: Learning curves—specificity and sensitivity among the epochs. Figure 6: Learning curves—precision and recall among the epochs. Specificity is a metric that describes the model's ability to correctly predict the true negative value. Specificity is given by the following formula [29]:  
\[Specificity=\frac{TN}{TN+FP}. \tag{5}\]  
Sensitivity is a metric that describes the model's ability to correctly predict the true positive value. Sensitivity is given by the following formula [29]:  
\[Sensitivity=\frac{TP}{TP+FN}. \tag{6}\]  
In this case, to determine the sensitivity, we first compute specificity at 200 different thresholds (\(threshold_{n}=\frac{threshold_{n-1}}{2};n=1,2,3,\ldots,200;threshold_{0}=1\)). Then, the sensitivity is computed at the chosen threshold [30]. When the model achieves high specificity and sensitivity values after the learning cycle, that means that the results are reliable. Precision is a ratio between the number of true positive samples and all samples classified as positive (i.e., the sum of true and false positive samples). This metric shows the model's ability to correctly classify positive samples.