## 4 Proposed Methodology
### Ensemble Learning
Figure 12 shows the performance comparison of three object detection models (YOLO8, RT-DETR, and Ensemble) on dental X-ray images for identifying various dental conditions. Here is the detailed analysis in \[\text{Detection Recall}=\frac{\text{Number of successful match}}{\text{Total Number of standard bounding box}} \tag{14}\]6 YOLO 8 provides reasonable detections but with lower confidence scores, especially for \"missing teeth\" and \"caries\". However, RT-DETR shows higher confidence scores for detections such as \"filling\" and \"impaired tooth\", providing more detailed annotations for \"caries\". Also, the Ensemble consistently shows high confidence scores for multiple conditions, including \"root canal treatment\", \"Caries\", and \"missing teeth\", making it the most comprehensive and accurate among the three models. Finally, this comparison indicates that the Ensemble model tends to provide more confident and detailed detections in dental X-ray images than both YOLO 8 and RT-DETR. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Position of Image** & **YOLO8** & **RT-DETR** & **Ensemble** \\ \hline - “Filling” with a confidence score of 0.79. & - “Filling” with scores of 0.91 and 0.43. & - “Filling” with a confidence score of 0.91. \\ - “Missing teeth” with confidence scores of 0.49 & - “Missing teeth” with a confidence score of 0.35. & - “Missing teeth” with a confidence score of 0.62. \\ Top Row & and 0.41. & - “Caries” with confidence scores of 0.29, 0.79, and 0.69. & 0.72. \\ \hline \multirow{4}{*}{Top Row} & - “Filling” with a confidence score of 0.32 & - “Impacted tooth” with a confidence score of 0.73. & - “Root canal treatment” with a confidence score of 0.73 and 0.78. \\ Middle Row & & & - “Missing teeth” with confidence scores of 0.65 \\ \cline{1-1} \cline{2-4}  & & & - “Caries” with confidence scores of 0.85, 0.81, and 0.82. \\ Bottom Row & - “Missing teeth” with a confidence score of 0.41. & - “Caries” with a confidence score of 0.32. & - “Missing teeth” with a confidence score of 0.63. \\ \cline{1-1} \cline{2-4}  & & & - “Caries” with a confidence score of 0.91. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Compares object detection models (YOLO8, RT-DETR) to real samples. Figure 11: Comparative analysis of the effectiveness and sensitivity of three models. ### Discussion

Table 3, titled \"Overall Performance\", provides a comprehensive comparison of the performance metrics of various object detection models, specifically focusing on the YOLO8 and RT-DETR models augmenting their Ensemble versions. The table is organized into distinct columns representing the different iterations of these datasets (V4, V5, V6), with an additional column for an ensemble that integrates the outputs of both models. Evaluative metrics include precision, recall, mean average precision at 50% IoU (mAP50), mean average precision averaged from 50% to 90% IoU (mAP50-90), and overall accuracy. Key observations reveal that while the precision of YOLO8 shows an increase from version V4 to V5 (from 50% to 74%), there is a decline in V6 (68%). Conversely, RT-DETR demonstrates a high precision peak in V5 at 81%, in addition to superior recall metrics. Notably, the Ensemble model in V6 outperformed all individual models, achieving remarkable metrics: 90% precision, 79% recall, and 92% accuracy. This analysis underscores the enhanced efficacy of Ensemble methods by capitalizing on the strengths of both object detection architectures, reflecting a more consistent performance trajectory, which is particularly evident in the improvements leading up to version V6. Thus, the accuracy improved by about 30% as the minimum value Yolo8 (V4) and maximum value Ensemble (V6). Finally, Table 4 presents a comparative examination of the performance indicators between the proposed Ensemble model and two other individual models, demonstrating the progressive improvements gained by Ensemble learning techniques.