## 4 Results
### Training Time  
The proposed model's training phase was executed with the aid of a GPU. The GPU programming approach leverages the GPU's processing units to perform the operations involved in the network's convolutional layers in parallel, reducing the processing time compared to sequential programming. Consequently, the GPU's hardware architecture directly impacts the time required per epoch.  
Extensive neural network architectures need a higher training time due to their number of parameters, and they are usually trained on specialized architectures that can be rented in terms of computing time. Consequently, the training time of an architecture is an essential factor to consider when it will be implemented in production. The proposed model has 7,939,778 parameters, representing a low number compared to other state-of-the-art methods that require specialized computer architectures with GPUs explicitly oriented to data processing for training, as observed in Table 9.  
This number of parameters allows the proposed model to be trained in high-end and mid-range general-purpose GPUs. The average time was recorded in Table 10 to assess this characteristic.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **MRI Modality** & **F1-Score** & **HD** & **Accuracy** & **Precision** & **Sensitivity** & **Specificity** \\ \hline [PERSON] et al. [9] & FLAIR & 0.7178 & 3.36 & - & - & - & - \\  & FLAIR-DWI & 0.7639 & **3.19** & - & - & - & - \\ \hline [PERSON] et al. [23] & DWI & 0.58 & 38.98 & - & 0.60 & 0.68 & - \\ \hline [PERSON] et al. [24] & DWI & 0.6220 & - & **0.9998** & - & 0.7322 & **0.9997** \\ \hline [PERSON] et al. [17] & Multi & 0.7008 & - & - & - & - & - \\ \hline [PERSON] et al. [20] & Multi & 0.7156 & - & - & - & - & - \\ \hline [PERSON] and [PERSON] [12] & Multi & 0.54 & - & - & 0.67 & 0.5 & - \\ \hline [PERSON] et al. [38] & Multi & 0.5577 & - & 0.9996 & **0.9977** & - & - \\ \hline [PERSON] et al. [19] & Multi & 0.7960 & - & 0.9956 & 0.9712 & - & - \\ \hline  & FLAIR & 0.8135 & - & 0.9673 & - & 0.8007 & 0.9962 \\ [PERSON] & DWI & 0.6928 & - & 0.9649 & - & 0.6069 & 0.9967 \\ et al. [18] & T1 & 0.7000 & - & 0.9651 & - & 0.6301 & 0.9961 \\  & T2 & 0.7072 & - & 0.965 & - & 0.6443 & 0.9961 \\ \hline  & FLAIR & 0.8289 & - & - & - & - & - \\ [PERSON] et al. [39] & DWI & 0.7029 & - & - & - & - & - \\  & T1 & 0.7015 & - & - & - & - & - \\  & T2 & 0.7368 & - & - & - & - & - \\ \hline  & FLAIR & **0.9129** & 4.09 & 0.9986 & 0.9152 & 0.9240 & 0.9992 \\  & DWI & 0.9096 & 4.13 & 0.9985 & 0.9084 & **0.9263** & 0.9991 \\  & T1 & 0.8916 & 5.12 & 0.9984 & 0.8870 & 0.9088 & 0.999 \\  & T2 & 0.8966 & 4.98 & 0.9984 & 0.8968 & 0.9092 & 0.9991 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the proposed method with the reported results of state-of-the-art methods on the ISLES 2015 dataset. The best values are given in bold text.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Criterion** & **[PERSON] et al. [25]** & **[PERSON] et al. [26]** & **[PERSON] et al. [27]** & **Proposed** \\ \hline F1-Score & 0.8560 & 0.6940 & 0.7869 & 0.7641 & 0.7517 & 0.6192 \\ MRI Modality & Multi & Multi & Multi & DWI & DWI & ADC \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of the proposed method with the reported results of state-of-the-art methods on the ISLES 2022 dataset.  
The coronal plane in the ISLES 2015 dataset has the highest training time among the other planes because its training set, being 953, is more extensive than in the other planes. Nevertheless, the training time does not exceed an hour using the mid-range GPU. On the other hand, the training set of the coronal plane in the ISLES 2022 dataset is around 4.7 times larger than the one in ISLES 2015, and its training is at most one and a half hours using the high-end GPU.