## 3 Experimental Results and Discussion
### Training and Evaluation of DCNNs
#### 3.2.1 ResNet-50 Classification Results  
The training performance of the ResNet-50 model for 30 epochs, with and without noise, is shown in Figure 9.  
Figure 9: ResNet-50 training performance for noisy and noiseless datasets.  
It is observed that under no noise conditions, the ResNet-50 achieved a maximum training and validation accuracy of 98.01% and 97.14%, respectively, with 0.133% training and 0.158% validation losses. Its performance degraded under noisy conditions, and for 20 dB noise, the training and validation accuracies decreased to 94.38% and 91.79%, respectively. The confusion matrices for noisy and noiseless datasets are illustrated in Figure 10, which demonstrates that the classification accuracy is relatively high for noiseless testing datasets as compared to other noisy datasets. The diagonal entries of the confusion matrix represent the true classification of testing instances, while the off-diagonal entries depict the misclassification of these samples. Additionally, accuracy, precision, recall, and F1-score were computed using Equations (4)-(7). The results indicate that ResNet-50 achieves superior performance on noiseless datasets, with an average value of accuracy (99.75%), precision (98.04%), recall (98%), and F1-score (98%), whereas with noisy test datasets, its performance decreases, resulting in an average value of accuracy (99.25%), precision (94.55%), recall (94%), and F1-score (94.13%) with 20 dB noise as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Without Noise**} & \multicolumn{4}{c}{**20 dB Noise**} & \multicolumn{4}{c}{**30 dB Noise**} & \multicolumn{4}{c}{**Random Noise**} \\ \cline{2-19} \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} \\ \hline ResNet-50 & 99.75 & 98.04 & 98 & 98 & 99.25 & 94.55 & 94 & 94.13 & 99.5 & 96.18 & 96 & 96.02 & 99.38 & 95.29 & 95 & 95.07 \\ VGG-16 & 99.48 & 96.17 & 95.88 & 95.90 & 99.13 & 93.50 & 93 & 93.07 & 99.39 & 95.36 & 95.13 & 95.16 & 99.25 & 94.26 & 94 & 94.05 \\ AlexNet & 99.38 & 95.21 & 95 & 95.01 & 98.91 & 91.73 & 91.25 & 91.23 & 99.08 & 93.23 & 92.63 & 92.71 & 98.94 & 99.05 & 91.49 & 91.60 \\ SuperseeNet & 98.75 & 90.75 & 90 & 90.01 & 98.59 & 99.31 & 88.87 & 88.75 & 98.86 & 90.10 & 90.25 & 98.30 & 98.59 & 98.91 & 88.87 & 88.75 \\ ResNet-50 with SE mechanism & 99.86 & 98.46 & 98 & 98.23 & 99.35 & 94.66 & 94 & 94.33 & 99.5 & 96.22 & 96 & 96.11 & 99.68 & 95.51 & 95 & 95.25 \\
**Voting Ensemble** & **99.98** & **99.97** & **99.80** & **99.85** & **99.73** & **98.23** & **97.78** & **99.90** & **99.83** & **99.65** & **99.80** & **99.88** & **98.68** & **98.10** & **98.05** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance evaluation of DCNNs and voting ensemble technique for noisy and noiseless test dataset.  
Figure 10: ResNet-50 confusion matrices for noisy and noiseless testing datasets.