## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & \multicolumn{5}{c}{**Performance Evaluation Metrics**} \\ \cline{2-6}
**Algorithms** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Time (s)** \\ \hline
**DT** & 0.8689 & 0.8717 & 0.8703 & 0.8705 & 2723.95 \\ \hline
**SVM** & 0.9422 & 0.9553 & 0.9487 & 0.9484 & 34,231.91 \\ \hline
**NB** & 0.8082 & 0.8767 & 0.8411 & 0.8343 & 11.90 \\ \hline
**KNN** & 0.8469 & 0.9476 & 0.8944 & 0.8881 & 5029.97 \\ \hline \end{tabular}
\end{table} 
Table 3: Classification results using ResNet50 features. 83.11%, recall of 88.67% and 96.02%, f1-score of 85.72% and 89.10% and accuracy of 85.22% and 88.25%, respectively. The result in Table 5 shows performance outputs of all the four classification algorithms obtained from the DenseNet121 features. DT and SVM achieved precision of 87.45% and 92.55%, recall of 86.82% and 96.15%, f1-score of 87.13% and 94.32%, and accuracy of 87.08% and 94.21%, respectively. The precision for NB and KNN classifiers are 71.67% and 79.77%, recall of 73.79% and 89.27%, f1-score of 72.71% and 84.26% and accuracy of 72.31% and 83.00%, respectively. The result in Table 6 shows performance outputs of all the four classification algorithms obtained from the DenseNet201 features. DT and SVM achieved precision of 86.60% and 92.93%, recall of 86.14% and 95.99%, f1-score of 86.37% and 94.43%, and accuracy of 86.33% and 94.34%, respectively. The precision for NB and KNN classifiers are 68.05% and 77.95%, recall of 83.96% and 93.93%, f1-score of 75.17% and 85.20% and accuracy of 72.27% and 83.68%, respectively. Figure 3 depicts the comparison of each classification algorithm based on accuracy. The SVM classifier performed effectively well than the rest of the algorithms and VGG16 features carry strong discriminatory information. In terms of computational efficiency, we keep track of the training time of each classification algorithm in seconds. Tables 1-6 show the respective training times using VGG16, VGG19, ResNet50, ResNet101, DenseNet121, and DenseNet201 features, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & \multicolumn{5}{c}{**Performance Evaluation Metrics**} \\
**Algorithms** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Time (s)** \\ \hline DT & 0.8660 & 0.8614 & 0.8637 & 0.8633 & 3612.66 \\ \hline SVM & 0.9293 & 0.9599 & 0.9443 & 0.9434 & 50.593.37 \\ \hline NB & 0.6805 & 0.8396 & 0.7517 & 0.7227 & 12.96 \\ \hline KNN & 0.7795 & 0.9393 & 0.8520 & 0.8368 & 4246.80 \\ \hline \end{tabular}
\end{table} 
Table 6: Classification results using DenseNet201 Features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & \multicolumn{5}{c}{**Performance Evaluation Metrics**} \\
**Algorithms** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Time (s)** \\ \hline DT & 0.8893 & 0.8840 & 0.8845 & 0.8848 & 3315.88 \\ \hline SVM & 0.9411 & 0.9560 & 0.9485 & 0.9481 & 30,682.47 \\ \hline NB & 0.8295 & 0.8867 & 0.8572 & 0.8522 & 11.86 \\ \hline
**KNN** & 0.8311 & 0.9602 & 0.8910 & 0.8825 & 4988.34 \\ \hline \end{tabular}
\end{table} 
Table 4: Classification results using ResNet101 features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & \multicolumn{5}{c}{**Performance Evaluation Metrics**} \\
**Algorithms** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Time (s)** \\ \hline DT & 0.8745 & 0.8682 & 0.8713 & 0.8708 & 1607.55 \\ \hline SVM & 0.9255 & 0.9615 & 0.9432 & 0.9421 & 26,035.99 \\ \hline NB & 0.7167 & 0.7379 & 0.7271 & 0.7231 & 6.91 \\ \hline KNN & 0.7977 & 0.8927 & 0.8426 & 0.8300 & 2671.86 \\ \hline \end{tabular}
\end{table} 
Table 5: Classification results using DenseNet21 Features. Figure 4 shows that the SVM is computationally inefficient compared to the rest of the classifiers during the training stage, while NB is more efficient computationally than the rest of the classifiers.