## 3 Results and Discussion of CNN Models Classification
### Results for VGG16 Model

Table 5: Detailed evaluation metrics for VGG16 model. Critical errors refer to the misclassification of instances where the model incorrectly identifies a \"Green\" drill hole as \"Red\" or vice versa. Such errors are particularly concerning due to their potential implications in real-world applications. * Fold 1--the model demonstrated a relatively high accuracy of 71.00%. Critical errors included one instance where a \"Green\" hole was classified as \"Red\" and three instances where \"Red\" holes were classified as \"Green\". The total number of critical errors in this fold was four. * Fold 2--the accuracy for this fold was 70.82%. Critical errors increased significantly, with eight instances of \"Green\" holes misclassified as \"Red\", and 11 instances of \"Red\" holes misclassified as \"Green\". The total number of critical errors in this fold was 19. * Fold 3--this fold showed a lower accuracy of 63.98%. There was one instance where a \"Green\" hole was misclassified as \"Red\", and a substantial number of 58 instances where \"Red\" holes were misclassified as \"Green\". The total number of critical errors was 59. * Fold 4--the accuracy dropped to 56.13% in this fold. A significant number of 61 instances where \"Green\" holes were classified as \"Red\" were observed. No instances of \"Red\" holes misclassified as \"Green\" were recorded. The total number of critical errors was 61. * Fold 5--the accuracy for this fold was 70.77%. Critical errors included one instance where a \"Green\" hole was classified as \"Red\", and 15 instances where \"Red\" holes were classified as \"Green\". The total number of critical errors in this fold was 16. The analysis of critical errors reveals several insights into the performance of the VGG16 model:  
1. Consistency in Green to Red misclassification--across all folds, there is a consistent occurrence of \"Green\" holes being misclassified as \"Red\". This misclassification is particularly problematic, as it could lead to unnecessary corrective actions or rework. 2. Variation in Red to Green misclassification--the misclassification of \"Red\" holes as \"Green\" varies significantly across folds. For instance, Fold 4 had no such misclassifications, while Fold 3 had a substantial number (58 instances). 3. Impact on overall accuracy--the high number of critical errors, especially in certain folds, significantly impacts the overall accuracy of the model. Folds with higher critical errors generally correspond to lower accuracy rates. 4. Need for model improvement--the analysis suggests that while the VGG16 model shows promise, there is a need for further refinement to reduce the critical error rate. Figure 8: Overall confusion matrix for all folds for VGG16 model. This could involve tuning the model parameters, using more advanced architectures, or incorporating additional data preprocessing techniques. By addressing these critical errors, the reliability and applicability of the VGG16 model for classifying drilled holes in melamine-faced chipboard can be enhanced, leading to more consistent and accurate results in practical applications.