## 4 Results
### Ablation Testing  
In order to assess the impact of various components of the proposed model on its overall performance, an ablation test was conducted using the same training, validation, and testing sets described in Section 3.2.1 for all the model's versions on the axial FLAIR modality. The details of each model version are described in Table 3.  
Figure 4 compares the learning curves. The k-fold iteration of each model's version with the median values in IoU was selected for this display. It is worth noting that the proposed model follows an adequate relation between the train and validation losses without presenting signs of overfitting or underfitting. The CBAM component involves calculating attention maps across the channel and spatial dimensions and is usually employed for general-purpose segmentation. On the other hand, the attention module given in Figure 1 applies attention through learned gating. The ablation study on the attention modules shows that the CBAM model and the one without attention modules (W/O A. M.) presented a faster convergence than the proposed model. However, the W/O A. M. model obtained the lowest IoU results after SGD, highlighting the contribution of attention modules to the proposed system. The CBAM model achieved marginally lower IoU and F1 scores than the proposed approach but fewer N. S. images in the small category. However, it increased the N. S. in the Medium Down category. Regarding the ablation study on the loss functions, the version with Focal Loss achieved a faster convergence and a better HD score than Dice Loss and the proposed model. Although a lower HD value is desirable in the segmentation task, the Focal Loss version produced more N. S. than the proposed method. Moreover, the proposed approach performed best in IoU and F1-Score metrics. A visual comparison between the model's version is given in Figure 5 to complement this ablation test. The results are ordered following the mask's size categories: Small (first row), Medium Down (second row), Medium Up (third row), and Large (fourth row). The cases are identified in the ground truth mask image using the nomenclature \(P\) C_S, where \(P\) refers to the plane, \(C\) to the subject's case number, and \(S\) to the slice. This figure shows slight differences in the number of FP and FN pixels: the Dice Loss and W/O A.M. versions present more FP pixels than the proposed model, while Focal Loss results in a narrowly higher number of FN. Furthermore, the CBAM model introduced more FP than the proposed approach but less than the W/O A.M. model, as seen in the case AXI 4_80. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **Proposed** & **W/O A. M.** & **CBAM** & **Dice Loss** & **Focal Loss** & **SGD** \\ \hline IoU & 0.8596 \(\pm\) 0.1598 & 0.8239 \(\pm\) 0.1593 & 0.8553 \(\pm\) 0.1601 & 0.8258 \(\pm\) 0.1798 & 0.8300 \(\pm\) 0.1797 & 0.4004 \(\pm\) 0.2796 \\ F1-Score & 0.9129 \(\pm\) 0.1362 & 0.8917 \(\pm\) 0.1364 & 0.9106 \(\pm\) 0.1345 & 0.8893 \(\pm\) 0.1570 & 0.8922 \(\pm\) 0.1549 & 0.5079 \(\pm\) 0.3244 \\ HD & 4.09 \(\pm\) 7.67 & 5.19 \(\pm\) 8.27 & 4.24 \(\pm\) 8.17 & 4.94 \(\pm\) 8.97 & 3.77 \(\pm\) 6.59 & 31.24 \(\pm\) 23.98 \\ N. S. & 4.10 \(\pm\) 2.70 & 4.30 \(\pm\) 2.65 & 3.20 \(\pm\) 2.27 & 6.10 \(\pm\) 4.04 & 5.80 \(\pm\) 1.78 & 86.10 \(\pm\) 2.98 \\ N. S. Small & 3.80 \(\pm\) 2.52 & 4.00 \(\pm\) 2.68 & 2.70 \(\pm\) 2.28 & 5.80 \(\pm\) 3.99 & 5.40 \(\pm\) 1.91 & 70.60 \(\pm\) 1.50 \\ N. S. M. D. & 0.30 \(\pm\) 0.90 & 0.30 \(\pm\) 0.64 & 0.40 \(\pm\) 0.80 & 0.30 \(\pm\) 0.90 & 0.40 \(\pm\) 0.92 & 15.50 \(\pm\) 2.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of the model’s version used for the ablation experiment in format mean \(\pm\) standard deviation.