## 4 Results and Discussion
### Comparison of Proposed Models Among Themselves
Among the folds, fold-4 reached the highest classification accuracy with a value of 0.9977. Among the eight proposed models, the ResNet-50 model was the best-performing CNN in this study. The performance evaluation of the ResNet-50 model is presented in Table 5. Performance evaluation accuracies were found to be 1.00 for fold-1, 1.00 for fold-2, 1.00 for fold-3, 0.9907 for fold-4, and 0.9907 for fold-5, and the average accuracy was 0.9963. Among the folds, fold-1, fold-2, and fold-3 reached the highest classification accuracy with a value of 1.00. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Networks**} & \multicolumn{6}{c}{**Mean Performance Metrics of 5-Fold CV**} \\ \cline{2-7}  & **Accuracy** & **F1-Score** & **Precision** & **Sensitivity** & **Specificity** & **ROC\_AUC** \\ \hline AlexNet & 0.9488 & 0.9466 & 0.9553 & 0.9426 & 0.9426 & 0.9936 \\ GoogLeNet & 0.9679 & 0.9669 & 0.9712 & 0.9639 & 0.9639 & 0.9957 \\ Proposed & 0.9679 & 0.9672 & 0.9668 & 0.9678 & 0.9678 & 0.9952 \\ MSU-CNN & 0.979 & 0.9786 & 0.9791 & 0.9784 & 0.9784 & 0.9982 \\ VGG-16 & 0.9888 & 0.9886 & 0.9878 & 0.9894 & 0.9894 & 0.9995 \\ EfficientNet-b0 & 0.9902 & 0.99 & 0.9901 & 0.9899 & 0.9899 & 0.9993 \\ Inception-v3 & 0.9921 & 0.9919 & 0.9916 & 0.9923 & 0.9923 & 0.9997 \\ ResNet-50 & 0.9963 & 0.9978 & 0.9935 & 0.9984 & 0.9956 & 0.9997 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics of all models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fold** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F1-Score** & **ROC\_AUC** \\ \hline
2 & 1 & 1 & 1 & 1 & 1 & 1 \\
3 & 1 & 1 & 1 & 1 & 1 & 1 \\
4 & 0.9907 & 0.9945 & 0.9837 & 0.9959 & 0.9891 & 0.9999 \\
5 & 0.9907 & 0.9945 & 0.9836 & 0.9959 & 0.989 & 0.9987 \\ Mean & 0.9963 & 0.9978 & 0.9935 & 0.9984 & 0.9956 & 0.9997 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance evaluation of the proposed ResNet-50 model. The training accuracy and loss graph of fold-5 for the ResNet-50 model is shown in Figure 4. In the training accuracy graph, the model's learning speed, the improvement in its accuracy, and its ability to generalize during training can be observed. On the other hand, the loss graph shows the rate at which the model's errors decrease, the speed of its progress, and potential issues, such as overfitting or underfitting. The confusion matrix with 5-fold CV and ROC curves with 5-fold CV for the ResNet-50 model are given in Figures 5 and 6, respectively. These figures regarding the classification performance of the proposed ResNet-50 model allow us to make inferences, such as evaluating model accuracy and detecting misclassifications. For example, for fold-4, the confusion matrix results indicate that the model correctly classified 245 instances of the 'Kirmizi' class and 181 instances of the 'Siirt' class. However, 1 'Kirmizi' sample was misclassified as 'Siirt', and 3 'Siirt' samples were incorrectly predicted as 'Kirmizi'. The classification accuracy values of the models used are presented in Figure 7. AlexNet, GoogLeNet, proposed MSU-CNN, VGG16, EfficientNet-b0, ResNet-18, Inception-v3, and ResNet-50 models achieved accuracy values of 0.9488, 0.9679, 0.9679, 0.9790, 0.9888, 0.9902, 0.9921, and 0.9963, respectively. Accordingly, the ResNet-50 model achieved the highest accuracy. It was followed by Inception-v3, ResNet-18, EfficientNet-b0, VGG16, the proposed MSU-CNN, GoogLeNet, and AlexNet, respectively. The F1-scores of the classification models is shown in Figure 8. The F1-score offers a balanced assessment of both precision and sensitivity. If either of these metrics is significantly low, the F1-score will also reflect a low value. The AlexNet, GoogLeNet, proposed MSU-CNN, VGG16, EfficientNet-b0, ResNet-18, Inception-v3, and ResNet-50 models achieved F1-scores of 0.9466, 0.9669, 0.9672, 0.9786, 0.9886, 0.99, 0.9919, and 0.9978, respectively. According to these results, the ResNet-50 model was the best at balancing precision and sensitivity.