## 3 Results
### Strain Map Prediction Accuracy
Concerning the SSIM of predicted strain maps, Figure 8d illustrates that an ensemble using two distinct U-Net architectures outperforms other methods. Figure 7: Strain map predicted from U-Net and cGAN architecture is based on 1000 FEM analyses of 2D arterial wall images, augmented to 4000 training images, (**a**) Test data image with true strain map (FEM) compared to U-Net and cGAN-generated maps. cGAN outperforms U-Net with SSIM 0.803 and 5.34% mean error. In comparison, U-Net has SSIM 0.822 and 6.35% mean error, (**b**) Comparison of strain map predictions between the best ensemble models and the basic U-Net and cGAN models, highlighting the superior performance of the ensemble U-Net (SSIM = 0.833), also, cGAN demonstrated superior performance in detecting higher strain values compared to U-Net, as highlighted by the black dashed box. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **\#of Models** & **Metrics** & **Min** & **Max** & **Mean** \\ \hline \multirow{3}{*}{U-Net} & \multirow{3}{*}{\(1\)} & MSE & 0.0120 & 0.037 & 0.022 \\  & & SSIM & 0.750 & 0.866 & 0.814 \\ \hline \multirow{3}{*}{cGAN} & \multirow{3}{*}{\(1\)} & MSE & 0.010 & 0.029 & 0.019 \\  & & SSIM & 0.751 & 0.842 & 0.793 \\ \hline \multirow{3}{*}{Ensembling (U-Net)} & \multirow{3}{*}{\(2\times 3\)} & MSE & 0.009 & 0.034 & 0.019 \\  & & SSIM & 0.800 & 0.877 & 0.825 \\  & & MSE & 0.009 & 0.025 & 0.018 \\  & & SSIM & 0.8153 & 0.877 & 0.830 \\ \hline \multirow{3}{*}{Ensembling (cGAN)} & \multirow{3}{*}{\(2\)} & MSE & 0.011 & 0.026 & 0.017 \\  & & SSIM & 0.785 & 0.839 & 0.800 \\ \cline{1-1}  & & MSE & 0.011 & 0.021 & 0.017 \\ \cline{1-1}  & & SSIM & 0.790 & 0.839 & 0.803 \\ \hline \multirow{3}{*}{Transfer Learning (U-Net)} & MSE & 0.012 & 0.029 & 0.020 \\ \cline{1-1}  & & SSIM & 0.790 & 0.868 & 0.820 \\ \hline \multirow{3}{*}{Transfer Learning (cGAN)} & MSE & 0.010 & 0.029 & 0.019 \\ \cline{1-1}  & & SSIM & 0.760 & 0.842 & 0.794 \\ \hline \end{tabular}
\end{table} 
Table 4: Different approaches for predicting strain maps on the testing data. Figure 8: Comparison of MSE and SSIM for all 600 validation images, (**a**) MSE of all test data for prediction Stress maps using U-Net, Ensembling with same U-Net and different hyper parameters, and ensembling with two U-Net architectures, cGAN, ensembling with same cGAN generator model and different hyper parameters, and Ensembling with two cGAN generator architectures, (**b**) SSIMof all test data for prediction Stress maps using U-Net, Ensembling with same U-Net and different hyper parameters, and Ensembling with two U-Net architectures, cGAN, Ensembling with same cGAN generator model and different hyper parameters, and Ensembling with two cGAN generator architectures, (**c**) MSE of all test data for prediction Strain maps using U-Net, Ensembling with same U-Net, Ensembling with same U-Net and different hyper parameters, ensembling with two U-Net architectures, U-Net using transfer learning, cGAN, ensembling with same cGAN generator and different hyper parameters, ensembling with two cGAN generator architectures, and cGAN using transfer learning (**d**) SSIM of all test data for prediction Strain maps using U-Net, Ensembling with same U-Net and different hyper parameters, Ensembling with two U-Net architectures, U-Net using transfer learning, cGAN, ensembling with same cGAN generator and different hyper parameters, ensembling with two cGAN generator architectures, and cGAN using transfer learning.