## 3 Results and Discussion of CNN Models Classification
### Results for VGG19 Model  
The evaluation results for the VGG19 model are presented in Table 6. The model's accuracy varied across the folds, with the highest accuracy of 71.60% observed in the fifth fold and the lowest accuracy of 52.38% observed in the fourth fold. The overall accuracy across all folds was 67.03%. The evaluation metrics for the VGG19 model, as shown in Table 7, provide an in-depth analysis of the model's performance across the \"Green\", \"Yellow\", and \"Red\" classes. The table includes metrics such as precision, sensitivity, specificity, accuracy, and F1 score, which give a comprehensive understanding of the model's classification capabilities. For the \"Green\" class, the model exhibits strong performance, with a precision of 84.86%, sensitivity of 78.89%, and an F1 score of 81.77%. The high values for specificity (88.79%) and accuracy (84.40%) suggest that the model effectively identifies and classifies instances belonging to the \"Green\" category, with minimal misclassifications. The \"Yellow\" class shows moderate performance, with a precision of 51.51% and sensitivity of 58.32%. The F1 score of 54.71% indicates that the model struggles with distinguishing \"Yellow\" instances, often confusing them with the other classes. The specificity of 73.16% and accuracy of 68.29% further highlight the challenges the model faces in correctly classifying \"Yellow\" instances, resulting in a higher rate of misclassification. For the \"Red\" class, the model achieves a precision of 59.72% and a sensitivity of 56.53%. Although the specificity is high at 88.72%, indicating a good ability to correctly exclude non-\"Red\" instances, the F1 score of 58.08% reflects the model's difficulty in accurately classifying \"Red\" instances. The accuracy for the \"Red\" class stands at 81.37%, suggesting some degree of reliability, though the model still struggles with edge cases. In summary, while the VGG19 model shows robust performance for the \"Green\" class, it has noticeable difficulties in accurately classifying the \"Yellow\" and \"Red\" classes. These results point to the need for further fine-tuning and model improvements, particularly for intermediate and heavily worn drill conditions. \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 & 69.50\% & 2 & 2 & 4 \\
2 & 70.76\% & 19 & 5 & 24 \\
3 & 70.59\% & 1 & 10 & 11 \\
4 & 52.38\% & 57 & 0 & 57 \\
5 & 71.60\% & 0 & 11 & 11 \\ Total & 67.03\% & 79 & 28 & 107 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation results of VGG19 model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Precision (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **Accuracy (\%)** & **F1 Score (\%)** \\ \hline Green & 84.86 & 78.89 & 88.79 & 84.40 & 81.77 \\ Yellow & 51.51 & 58.32 & 73.16 & 68.29 & 54.71 \\ Red & 59.72 & 56.53 & 88.72 & 81.37 & 58.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Detailed evaluation metrics for VGG19 model. The confusion matrix of the VGG19 model, depicted in Figure 9, offers a comprehensive analysis of its classification accuracy across all folds. It details the count of both accurately and inaccurately classified instances by class. In the confusion matrix, the true positive rates for the classes \"Green\", \"Yellow\", and \"Red\" are as follows:  
* The \"Green\" class had 2982 correctly classified instances, with 719 instances misclassified as \"Yellow\" and 79 instances as \"Red\". * The \"Yellow\" class had 1633 correctly classified instances, with 504 instances misclassified as \"Green\" and 663 instances as \"Red\". * The \"Red\" class had 1100 correctly classified instances, with 818 instances misclassified as \"Yellow\" and 28 instances as \"Green\". These results indicate that the VGG19 model achieved moderate accuracy, with varying degrees of misclassification among the classes.