## 5 Discussion  
The results presented in the previous section reveal several key insights, which are discussed in this section to help readers better understand the proposed approach's principal features. As was mentioned in the related works Section 2, the principal challenge in segmenting ischemic stroke lesions in MRI images lay in the class imbalance resulting from the high disparity between the number of pixels labeled as foreground and the background of the images. An example of this situation can be found in MRI images depicting small lesions. Consequently, diverse state-of-the-art methods struggle to segment them. Different studies experiment with function losses that try to diminish the impact of the class imbalance in the training phase, such as [PERSON] et al. [17] and [PERSON] et al. [23]. Both explored Cross-Entropy and Dice loss functions, which contemplate the probability of the classes mitigating the contribution of the class with major influence. In this work, the proposed method was tested utilizing the General Dice loss and the Focal loss functions, the last one being a ponderation of the cross-entropy loss. The results demonstrate that using the General Dice Focal loss improves segmentation results because the number of non-segmented images is reduced. Moreover, the attention module helped increase the IoU of the segmented images by 0.03 and reduce the HD by 1.09, giving more pixel precision and competing with the method of [PERSON] et al. [9] that also employs an attention module but based on dilated soft mask. Furthermore, the detail of the IoU and F1 scores of small stroke lesions in Figure 7 demonstrates that the proposed approach reached an IoU greater than 0.8 using FLAIR, DWI, and T2 sequences. This premise is based on the median values and the distribution of the violin plots presented. Besides, this behavior is repeated for the segmentation in the coronal and sagittal plane. Regarding the performance on another dataset, such as ISLES 2022, it can be observed that the average IoU is lower than the one achieved in the ISLES 2015 dataset in the DWI modality. However, the F1-Scores of 0.7517, 0.5833, and 0.5636 for the axial, coronal, and sagittal planes still represent an acceptable output given the value ranges for the ISLES 2015 reported in Table 7, where the lower value for DWI was 0.58. A point worth noting is that the Figures 9f and 13f,g,j,k reveal the struggle of the proposed model to segment targets with isolated pixels accompanied by pixel blobs of more significant size. This behavior is \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **GPU Model** & **Axial** & **Coronal** & **Sagittal** \\ \hline ISLES 2015 & \begin{tabular}{c} GeForce RTX 3070 \\ GeForce RTX 3090 \\ \end{tabular} & \begin{tabular}{c} 34 m 32 s \\ 16 m 26 s \\ \end{tabular} & \begin{tabular}{c} 41 m 08 s \\ 20 m 07 s \\ \end{tabular} & \begin{tabular}{c} 24 m 55 s \\ 11 m 59 s \\ \end{tabular} \\ \hline ISLES 2022 & \begin{tabular}{c} GeForce RTX 3090 \\ \end{tabular} & \begin{tabular}{c} 53 m 26 s \\ \end{tabular} & \
\begin{tabular}{c} 1 h 16 m 37 s \\ \end{tabular} &
\begin{tabular}{c} 1 h 16 m 39 s \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Average training time of the proposed model in different datasets and planes. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Parameters (M)** \\ \hline [PERSON] et al. [25] & 119.0 \\ [PERSON] et al. [26] & 22.3 \\ Proposed & 7.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Complexity comparison with state-of-the-art models. also reported in the work of [PERSON] et al. [23]. However, the proposed model successfully segments diminutive lesions, such as the one presented in Figure 13e,i. The proposed model's performance on ISLES 2022 using two combinations of hyperparameters concerning the loss function was analyzed. The weights assigned to the FL and GDL functions directly impacted the number of non-segmented images and the predicted segmentation mask's F1-Scores.