## 4 Results and Discussion  
When using only the FLAIR modality, the U-Net with ResNeXt-50 yielded the best performance during training compared to the other models. Since each model had reached asymptotic behavior with respect to the aggregate loss, the DSC, and IoU at the end of the training step, any change or increase in the number of epochs would not change the outcome of training (Figure 4). In some cases, other models had obtained its optimal performance before reaching the last epoch, which indicates that an increase in the epoch size could counteract the training because the model would overfit the training data. Regardless, each model reached higher DSC and IoU scores when using the FLAIR sequence. Additionally, since the weights that yielded the highest DSC had been saved and used during testing, the testing metrics of the model are reliable for obtaining the optimal performance relative to a particular MRI sequence. However, because the training performance could change based on the amount of data, a larger dataset was considered for subsequent training and testing. Table 3 portrays the DSC and IoU for the four CENs when using each of the different inputs. The DSC and IoU metrics show that the FLAIR sequence yielded the best segmentation performance during testing across all models. When using FLAIR, the U-Net with ResNeXt-50 had obtained a DSC of 0.6678 and an IoU of 0.6223; this indicates that the U-Net CEN with a FLAIR MRI sequence performed the best compared to every other model-sequence combination. Additionally, models using the FLAIR\({}^{2}\) sequence did not outperform models using the FLAIR sequence. However, they provided better segmentations than when using the T2 sequence. A possible explanation for this could be the result of relying on a single specific scanner. Additionally, a recent study has experimentally found a different multi contrast combination known as FLAIR\({}_{3}\)[41]. This combination is created by multiplying FLAIR\({}^{1.55}\)\(\times\) T\({}^{2.145}\), and it has been demonstrated to create higher contrast [41]. Thus, it would be worthwhile to investigate the utilization of FLAIR in future works. It is important to note the differences between the U-Net++ CEN and the U-Net CEN. In the case of T2 sequences, the U-Net++ outperformed the U-Net with respect to DSC by approximately 0.02 points. However, in the case of T1 sequences, the U-Net++ CEN did much worse relative to the other models with a DSC of 0.1072 and an IoU of 0.1066. When analyzing the training of the U-Net++ CEN using the T1 sequence, both the DSC and U-Net++ CEN are able to achieve the best performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input** & **Model** & **DSC** & **IoU** \\ \hline \multirow{4}{*}{T1} & FPN & 0.5018 & 0.4835 \\  & Linknet & 0.4060 & 0.3988 \\  & U-Net & 0.4972 & 0.4703 \\  & U-Net++ & 0.1072 & 0.1066 \\ \hline \multirow{4}{*}{T2} & FPN & 0.5519 & 0.5277 \\  & Linknet & 0.4948 & 0.4948 \\  & U-Net & 0.5612 & 0.5268 \\  & U-Net++ & 0.5814 & 0.5453 \\ \hline \multirow{4}{*}{FLAIR} & FPN & 0.6250 & 0.5851 \\  & Linknet & 0.5988 & 0.5880 \\ \cline{1-1}  & U-Net & 0.6678 & 0.6223 \\ \cline{1-1}  & U-Net++ & 0.6677 & 0.6222 \\ \hline \multirow{4}{*}{FLAIR\({}^{2}\)} & FPN & 0.5153 & 0.3740 \\ \cline{1-1}  & Linknet & 0.5652 & 0.4105 \\ \cline{1-1}  & U-Net & 0.5497 & 0.3932 \\ \cline{1-1}  & U-Net++ & 0.5614 & 0.4038 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Testing Metrics Using UMCL Dataset. Figure 4: U-Net with ResNeXt-50 training with FLAIR sequence. Shown are the DSC, IoU, and loss per epoch during training. and IoU plateau and the loss function reaches asymptotic behavior (Figure 5). Thus, it is most likely not the case that training the model with more data could ameliorate the testing performance. A possible explanation for these differences is that the U-Net++ adds more intermediate skip connections with the intent of remedying dissimilar feature maps between the encoding and decoding steps.