## 4 Proposed Methodology
### Ensemble Learning
Figure 6 shows the overall performance for YOLO8, where the curve labeled \"all classes 0.584 mAP0.5\" shows the average performance across all classes, providing a general sense of the model's effectiveness. Figure 7 helps to understand the effectiveness of a classifier across different thresholds and for various conditions. This type of analysis is crucial in fields such as medical imaging, where accurate and reliable predictions can significantly impact patient outcomes. Figure 4: Precision and recall metrics for YOLO8 and RT-DETR. Figure 5: mAP50 and mAP50-95 metrics for YOLO8 and RT-DETR. In the RT-DETR, the precision-recall curves help in understanding which classes are being detected more accurately and reliably and which ones may require further training or adjustment in the model parameters. The overall performance across all classes (indicated by the blue line labeled \"all classes 0.736 mAP0.5\") suggests a moderate level of accuracy when considering all detections collectively [37]. This chart is essential for evaluating and refining the RT-DETR, ensuring it is optimized for the specific detection tasks it is intended to perform, as shown in \[\text{Detection Recall}=\frac{\text{Number of successful match}}{\text{Total Number of standard bounding box}} \tag{14}\]0

Figure 7(a) shows the Confusion Matrix Normalized for YOLO8, which is a visualization tool typically used to measure the performance of a classification model. Each cell in the matrix represents the proportion of predictions made by the model. For instance, the cell at the intersection of the \"Crown\" row and the \"Crown\" column shows 0.92, which means 92% of the actual Crown cases were correctly predicted as a Crown by the model. Off-diagonal cells indicate incorrect predictions. For example, the 0.21 in the \"Caries\" row and \"Crown\" column indicates that 21% of the cases that were actually Caries were incorrectly predicted as Crown. High Accuracy Categories like \"Implant\" (0.96) and \"Crown\" (0.92) show very high diagonal values, indicating that the model is highly accurate in predicting these conditions. Misclassifications such as \"Caries\" being mistaken for \"Crown\" (0.21) and \"Root Canal Treatment\" being confused with \"Root Piece\" (0.21). The performance of the model varies across different categories. Some categories, like \"Implant\", are predicted

\[\text{Detection Recall}=\frac{\text{Number of successful match}}{\text{Total Number of standard bounding box}} \tag{14}\]1 Precision-recall curve for YOLO8. \[\text{Detection Recall}=\frac{\text{Number of successful match}}{\text{Total Number of standard bounding box}} \tag{14}\]2 Precision-recall curve for RT-DETR. with high accuracy, while others, like \"Periapical Lesion\", have lower correct prediction rates (0.12). Figure 8(b) shows the normalized confusion matrix for an RT-DETR. Diagonal values represent the proportion of correctly predicted instances for each class. For example, \"Crown\" has a value of 0.96, indicating that 96% of the crown predictions were correct. Off-diagonal values show the proportion of incorrect predictions, where the model predicted the row label, but the true label was the column label. For example, under \"Caries\" (row) and \"Crown\" (column), there is a value of 0.02, indicating that 2% of the true \"Crown\" instances were incorrectly predicted as \"Caries\". High performers like \"Crown\" (0.96), \"Implant\" (0.97), and \"Root Piece\" (0.92) have high predictive accuracy, indicating effective recognition by the model. Misclassifications such as \"mandibular canal\" are often misclassified as \"missing teeth\" (0.15) and \"periapical lesion\" (0.37). \"Root Canal Treatment\" is sometimes confused with \"Retained Root\" (0.17) and \"Root Piece\" (0.18). Figure 9(a) shows a filling located in the upper left molar region. This indicates the presence of a dental filling with a confidence score of 0.79. Missing teeth in two areas are marked with this label, one on the left side and another on the right side of the lower jaw.