## 4 Experimental Results
### Performance Evaluations
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Models** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline Vgg16 & 91.13\% & 86.84\% & 98.51\% & 92.31\% \\ Vgg19 & 92.74\% & 88.16\% & 100\% & 93.71\% \\ Resnet50 & 81.45\% & 84.21\% & 85.33\% & 84.77\% \\ InceptionV3 & 91.13\% & 92.11\% & 93.33\% & 92.72\% \\ Densenet121 & 90.32\% & 88.16\% & 95.71\% & 91.78\% \\ Xception & 89.52\% & 88.16\% & 94.37\% & 91.16\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance results of the TL models. Figure 11: (**a**) Confusion matrix and (**b**) ROC curve results of the proposed FT-EPN model. Figure 10: Accuracy and loss curve results of the proposed FT-EPN model. The Vgg16 and Vgg19 models provided high test accuracy, achieving success rates of 91.13% and 92.74%, respectively, while the Resnet50 model performed lower than the other models with a rate of 81.45%. The InceptionV3, Densenet121, and Xception models also showed similar performance to the Vgg-based models, with accuracy rates of 91.13%, 90.32%, and 89.52%, respectively. Overfitting of the model was prevented, and a balanced learning process was achieved using techniques such as early stopping, the Tanh activation function, L2 regularization, and an optimal learning rate during the training process. Although some of the pre-trained TL models were observed to achieve higher performance than the Sequential-based model, they did not provide a significant improvement in the PCa classification problem. Furthermore, the performance results of the TL models were considerably lower compared to the proposed FT-EPN for PCaD. Figure 12: Accuracy and loss curve results of the TL models. Figure 13: Confusion matrix results of the TL models. The performance results of the proposed FT-EPN model are finally compared with the classification performances of the popular ViT and MaxViT-v2 hybrid models. Unlike traditional CNN-based approaches, the ViT model uses SAM to divide the image into small patches and model the relationship of each patch with the others. This approach allows for capturing global contexts in the image, providing more global feature learning. MaxViT combines the strengths of both CNN and ViT architectures, enabling it to learn local and global features simultaneously. The model successfully captures both local information and the image's global context due to the multi-axis attention mechanism. The confusion matrix and the ROC curve of the ViT architecture are shown in Figure 14. The confusion matrix and the ROC curve of the MaxViT-v2 architecture are shown in Figure 15. The performance results of the ViT and MaxViT-v2 transformer models are presented in Table 5.  
When Table 5 is examined, it can be seen that the ViT model achieves a classification accuracy of 93.55%, while the MaxViT-v2 model achieves 95.16%. Accordingly, it is observed that the MaxViT-v2 hybrid model demonstrates more effective performance compared to the ViT model and also provides higher classification success than all the pre-trained TL  
Figure 14: (a) Confusion matrix and (b) ROC curve results of the ViT architecture. Figure 15: (a) Confusion matrix and (b) ROC curve results of the MaxViT-v2 architecture. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Models** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline ViT & 93.55\% & 100\% & 85.71\% & 92.31\% \\ MaxViT-v2 & 95.16\% & 95.65\% & 91.67\% & 93.62\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance results of the transformer models. architectures. On the other hand, the proposed FT-EPN model in this study achieves 1.6% higher classification success compared to the high-performance MaxViT-v2.