## 4 Experimental Results
### Performance Evaluations  
In evaluating the performance of the proposed FT-EPN model for PCaD, other criteria were also examined alongside the classification accuracy rates. In this performance evaluation, the confusion matrices obtained from the models were used. Accuracy, precision, recall, and F1 score metrics were calculated using the confusion matrix. First, 90.32% classification accuracy was achieved with the developed Sequential-based model on the PCa MRI dataset using 10 epochs and the Adam optimization algorithm. The accuracy and loss curves of the Sequential-based model are shown in Figure 8, and the confusion matrix and ROC curve are shown in Figure 9.  
The accuracy obtained by the Sequential-based model on a limited dataset can be considered insufficient for real-world applications. For a more comprehensive evaluation, the performance of this model is further supported by different metrics, as shown in Table 2.  
Although the accuracy rate seems quite good when Table 2 is examined, it can be misleading, especially in unbalanced datasets, as the model tends to predict negative examples as positive. In addition, the difference between the precision and recall values is striking. This indicates that the model is less selective in its positive predictions and makes a large number of positive predictions. In our FT-EPN model, a pyramid architecture was used to learn complex features more effectively, and an increase in accuracy was achieved by adding convolutional layers before each MaxPooling layer. The L2 regularization technique was chosen to prevent overfitting, and the most appropriate regularization coefficient was determined to be 0.0001. It was observed that L2 regularization provided better results than dropout. The training process was optimized by using the Adam optimization algorithm with a learning rate of 0.0001, and training was terminated when no progress was made in the model, using the early stopping method. The Tanh activation function contributed to a more balanced learning process by providing a zero-centered distribution. The accuracy and loss curves of the FT-EPN model are shown in Figure 10, and the confusion matrix and ROC curve are presented in Figure 11. The performance results are summarized in Table 3. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline Sequential-base model & 90.32\% & 84.21\% & 100\% & 91.43\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance results of the Sequential-base model. Figure 8: Accuracy and loss curve results of the Sequential-based model. Figure 9: (**a**) Confusion matrix and (**b**) ROC curve results of the Sequential-based model. The proposed FT-EPN model achieved 96.77% accuracy, representing a 7% increase compared to the Sequential-based model, according to the given ROC curves and confusion matrix analysis, and demonstrated strong predictive ability. The accuracy and overall performance of the model were evaluated using the pre-trained CNN architectures Vgg16, Vgg19, Resnet50, InceptionV3, Densenet121, and Xception, as mentioned in the Section 3.3 of this paper. Only the feature extraction layers of these models, pre-trained with the ImageNet dataset, were used, and the classification layers were re-trained. The accuracy and loss curves obtained with the TL strategy for each pre-trained model are shown in Figure 12, and the confusion matrices are shown in Figure 13. The performance results, calculated using the metrics for a comprehensive comparison of the classification success of the models, are given in Table 4.