## 3 Results and Discussion of CNN Models Classification
### Results for VGG16 Model  
The evaluation results for the VGG16 model are presented in Table 4. The model's accuracy varied across the folds, with the highest accuracy of 71.00% observed in the first fold and the lowest accuracy of 56.13% observed in the fourth fold. The overall accuracy across all folds was 66.60%. The evaluation metrics for the VGG16 model, as presented in Table 5, provide a detailed analysis of the model's performance across three classes: \"Green\", \"Yellow\", and \"Red\". The table highlights key indicators such as precision, sensitivity, specificity, accuracy, and F1 score. For the \"Green\" class, the model demonstrates strong performance with a precision of 79.58%, a sensitivity of 84.05%, and an F1 score of 81.76%. The high values in these metrics suggest that the model is effective in correctly identifying and classifying instances as \"Green\", with a balanced trade-off between precision and recall. In contrast, the \"Yellow\" class shows a significant drop in performance. The precision and sensitivity are 51.97% and 52.86%, respectively, with a corresponding F1 score of 52.41%. These results indicate that the model struggles to correctly classify instances in the \"Yellow\" category, leading to a higher number of misclassifications. The specificity for the \"Yellow\" class is higher at 76.11%, reflecting a better ability to distinguish \"Yellow\" instances from non-\"Yellow\" instances, but overall accuracy remains lower at 68.47%. The \"Red\" class also faces challenges, with a precision of 60.62% and a sensitivity of 52.52%. Although the specificity is high at 89.91%, the F1 score of 56.28% reflects the model's difficulty in accurately classifying instances as \"Red\", with a tendency to misclassify these instances as either \"Green\" or \"Yellow\". Overall, the table indicates that the model performs well for the \"Green\" class, but struggles to maintain the same level of accuracy and precision for the \"Yellow\" and \"Red\" classes. These findings highlight areas for improvement, especially in refining the classification of intermediate and highly worn drill holes. The VGG16 model's confusion matrix, depicted in Figure 8, offers an in-depth examination of its classification efficacy over various folds. This matrix displays the count of instances that were accurately and inaccurately categorized for each class. In the confusion matrix, the true positive rates for the classes \"Green\", \"Yellow\", and \"Red\" are as follows:  
* The \"Green\" class had 3177 correctly classified instances, with 531 instances misclassified as \"Yellow\" and 72 instances as \"Red\". * The \"Yellow\" class had 1480 correctly classified instances, with 728 instances misclassified as \"Green\" and 592 instances as \"Red\". * The \"Red\" class had 1022 correctly classified instances, with 837 instances misclassified as \"Yellow\" and 87 instances as \"Green\". These results indicate that the VGG16 model achieved moderate accuracy, with varying degrees of misclassification among the classes. The model showed a higher tendency to misclassify instances of the \"Yellow\" class, particularly as \"Green\" and \"Red\". \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fold / Drill \#**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{3}{c}{**Critical Errors**} \\ \cline{3-5}  & & **Green as Red** & **Red as Green** & **Total** \\ \hline
1 & 71.00\% & 1 & 3 & 4 \\
2 & 70.82\% & 8 & 11 & 19 \\
3 & 63.98\% & 1 & 58 & 59 \\
4 & 56.13\% & 61 & 0 & 61 \\
5 & 70.77\% & 1 & 15 & 16 \\ Total & 66.60\% & 72 & 87 & 159 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation results of VGG16 model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Precision (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **Accuracy (\%)** & **F1 Score (\%)** \\ \hline Green & 79.58 & 84.05 & 82.83 & 83.37 & 81.76 \\ Yellow & 51.97 & 52.86 & 76.11 & 68.47 & 52.41 \\ Red & 60.62 & 52.52 & 89.91 & 81.37 & 56.28 \\ \hline \hline \end{tabular}
\end{table}