## 6 Conclusions  
Early identification of ischemic strokes reduces the risk of permanent damage and increases the chances of recovery by allowing the implementation of effective medical interventions. The identification requires MRI imaging to evaluate the damage in cerebral tissue. However, annotation in the images represents a high workload for radiologists since it is manually performed and prone to potential human error. Therefore, automatic lesion segmentation could improve the diagnosis and treatment planning due to the delivery of a rapid evaluation of the images, which can assist radiologists in reducing their labor complexity and lead to an early diagnosis.  
The proposed method significantly improves the segmentation of ischemic stroke lesions in MRI images, particularly addressing the class imbalance challenge. The approach successfully mitigates the influence of background pixels by utilizing the Generalized Dice Focal loss function, leading to enhanced segmentation accuracy. Incorporating an attention module further improves performance, delivering competitive results compared to state-of-the-art methods.  
The method's effectiveness is highlighted by its ability to segment small stroke lesions with a high Intersection over Union (IoU), especially in FLAIR, DWI, and T2 sequences across different planes. Despite a lower average IoU in the ISLES 2022 dataset, the model still achieves acceptable F1-Scores, underscoring its robustness and reliability. These segmentation results can be leveraged as a first screening of the images for radiologists.  
Additionally, the proposed model exhibits efficient training due to a reasonable convergence at 200 epochs and relatively low training time, which makes it suitable for production environments. This efficiency highlights the practicality of the proposed approach for ischemic stroke lesion segmentation, which can be deployed using general-purpose GPUs in non-high-tech environments.  
Given the model's ability to segment lesions in the different anatomical planes of the MRI images and its lightweight feature, future research directives could focus on using the proposed model as a backbone to fuse the features of the anatomical planes to mitigate the error in segmenting isolated target pixels.  
Conceptualization, O.C.-C., B.P.G.-S. and V.P.; methodology, B.P.G.-S., J.A.-D. and O.C.-C.; software, B.P.G.-S., J.A.-A.-D. and O.C.-C.; validation, B.P.G.-S., J.A.-D. and V.P.; formal analysis, B.P.G.-S. and J.A.A.-D.; investigation, O.C.-C., B.P.G.-S. and J.A.A.-D.; resources, V.P., R.R.-R., C.C.-R. and S.S.; data curation, B.P.G.-S.; writing--original draft preparation, O.C.-C., J.A.A.-D. and V.P.; writing--review and editing, B.P.G.-S., V.P. and J.A.A.-D.; visualization, B.P.G.-S.; supervision, B.P.G.-S., J.A.A.-D., V.P., R.R.-R. and C.C.-R.; project administration, V.P., R.R.-R., C.C.-R. and S.S.; funding acquisition, V.P., R.R.-R. and C.C.-R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets used to evaluate the model are publicly available. The information for downloading them can be found in their corresponding references. The data and code presented in this study are available for academic purposes upon request from the corresponding author.  
The authors would like to thank the Instituto Politecnico Nacional (IPN) (Mexico), the Comision de Operacion y Fomento de Actividades Academicas (COFAA) of IPN, and the Consejo Nacional de Humanidades, Ciencias y Tecnologias (Mexico) for their support in this work.  
The authors declare no conflicts of interest.