## 4 Results and Discussion
### Comparison of Proposed Models Among Themselves  
The classification accuracies for each fold of the seven pre-trained CNN models with transfer learning, as well as for each fold of the proposed MSU-CNN model, are presented. Additionally, the average classification accuracy is calculated. According to these results, Resnet50 model was found to perform the best. Confusion matrices of each fold, ROC curves, and AUC values are provided for the best performing Resnet50. Furthermore, the training accuracy and loss graphs for one-fold the highest-performing Resnet50 model are also shown. The proposed MSU-CNN model ranked sixth in classification performance among the eight models, demonstrating its competitiveness compared to the other models. Mean performance metrics of all models are calculated and presented in Table 4.  
The classification accuracies of AlexNet model were found to be 0.9394 for fold-1, 0.9047 for fold-2, 0.9581 for fold-3, 0.9884 for fold-4, and 0.9534 for fold-5, and the average accuracy was 0.9488. Among the folds, fold-4 reached the highest classification accuracy with a value of 0.9884. The classification accuracies of GoogLeNet model were found to be 0.9674 for fold-1, 0.9605 for fold-2, 0.9837 for fold-3, 0.9605 for fold-4, 0.9674 for fold-5, and the average \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Network Models** & \begin{tabular}{c} **Training** \\ **Optimization** \\ **Algorithms** \\ \end{tabular} & **Epochs** & \
\begin{tabular}{c} **Batch** \\ **Size** \\ \end{tabular} & \multicolumn{3}{c}{**Collision**} & \multicolumn{3}{c}{**Accuracies of 5-Fold**} & \multicolumn{1}{p{56.9 pt}}{
\begin{tabular}{c} **Cross-Validation (\%)** \\ \end{tabular} } \\ \hline AlexNet & SGDM & 20 & 9 & 93.94 & 90.47 & 95.81 & 98.84 & 95.34 & 94.88 \\ GoogLeNet & SGDM & 10 & 11 & 96.74 & 96.05 & 98.37 & 96.05 & 96.74 & 96.79 \\ Proposed & Adam & 40 & 12 & 96.97 & 96.51 & 97.21 & 96.28 & 96.97 & 96.79 \\ MSU-CNN & SGDM & 8 & 9 & 96.97 & 98.14 & 97.44 & 99.07 & 97.90 & 97.90 \\ EfficientNet-b0 & SGDM & 8 & 9 & 99.53 & 98.60 & 99.30 & 98.60 & 98.37 & 98.88 \\ ResNet-18 & SGDM & 17 & 13 & 98.83 & 99.07 & 98.60 & 99.07 & 99.53 & 99.02 \\ Inception-v3 & SGDM & 8 & 9 & 99.30 & 99.07 & 98.60 & 99.77 & 99.30 & 99.21 \\ ResNet-50 & Adam & 15 & 16 & 100 & 100 & 100 & 99.07 & 99.07 & 99.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracy and training parameters of all models. accuracy was 0.9679. Among the folds, fold-3 reached the highest classification accuracy with a value of 0.9837. The classification accuracies of MSU-CNN model were found to be 0.9697 for fold-1, 0.9651 for fold-2, 0.9721 for fold-3, 0.9628 for fold-4, and 0.9697 for fold-5, and the average accuracy was 0.9679. Among the folds, fold-3 reached the highest classification accuracy with a value of 0.9721. The classification accuracies of VGG-16 model were found to be 0.9697 for fold-1, 0.9814 for fold-2, 0.9744 for fold-3, 0.9907 for fold-4, and 0.9790 for fold-5, and the average accuracy was 0.9790. Among the folds, fold-4 reached the highest classification accuracy with a value of 0.9907. The classification accuracies of EfficientNet-b0 model were found to be 0.9953 for fold-1, 0.9860 for fold-2, 0.9930 for fold-3, 0.9860 for fold-4, and 0.9837 for fold-5, and the average accuracy was 0.9888. Among the folds, fold-1 reached the highest classification accuracy with a value of 0.9953. The classification accuracies of ResNet-18 model were found to be 0.9883 for fold-1, 0.9907 for fold-2, 0.9860 for fold-3, 0.9907 for fold-4, and 0.9953 for fold-5, and the average accuracy was 0.9902. Among the folds, fold-5 reached the highest classification accuracy with a value of 0.9953. The classification accuracies of Inception-v3 model were found to be 0.9930 for fold-1, 0.9907 for fold-2, 0.9860 for fold-3, 0.9977 for fold-4, and 0.9930 for fold-5, and the average accuracy was 0.9921.