## 3 Results  
Five models (YOLOv8x-seg, YOLOv8x-seg-p6, YOLOv9c-seg, YOLOv9e-seg, YOLOv9e-seg, YOLOv11-seg) were trained for the segmentation of ROIs in the meniscus region using the existing YOLO algorithms YOLOv8, YOLOv9, and YOLOv11 series. In the rest of the paper, these models will be referred to as Model 1, Model 2, Model 3, Model 4, and Model 5, respectively. To improve the prediction results obtained from these five models, ensemble-methods-based approaches were used, and pixel-based voting, weighted multiple voting, and dynamic weighted multiple voting optimized by grid search were applied. In particular, dynamic weighted multiple voting with grid search significantly improved the model performance. R, P, and F1 score metrics were used to measure the performance of the models at the end of training. The best metric values (best) and the last epoch value (last) obtained by the models for 100 epochs in the mask generation task are given in Table 3 and Figure 4. When Table 4 and Figure 4 are carefully analyzed, Model 1 showed the highest performance in terms of best metric values with P (0.9379), R (0.9277), and F1 score (0.9328) values. Considering the last epoch values, Model 1 maintained its superiority over the other models, although it showed a slight decrease in P (0.9312), R (0.9084), and F1 score (0.9196) metrics. Figure 4 shows that during the training process, the parameter values of the models initially increase rapidly and then reach a stable point. In particular, Figure 4 shows that Model 1 reaches a stable performance in the early epoch values, while Model 5 fluctuates a lot during the training process. The models were trained on the internal dataset and evaluated with the test and external sets. Figure 5 shows the results of the qualitative images obtained. Table 4 shows the number of images from the test set and the external set for which the models failed to generate ROI areas during the evaluation. Model 1 successfully predicted ROI areas for all test and external set images. Model 2, on the other hand, fell behind the other models as the model with the highest number of unsegmented ROI areas. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Test Set** & **External Set** \\ \hline Model 1 & 0 & 0 \\ \hline Model 2 & 6 & 8 \\ \hline Model 3 & 0 & 1 \\ \hline Model 4 & 1 & 1 \\ \hline Model 5 & 3 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of unsegmented images in test and validation sets. Figure 4: Curves showing precision, recall, and F1 score values of the models over 100 epochs. (**a**) P value curve. (**b**) R value curve. (**c**) F1 score value curve. The trained models were evaluated in the test set and external set with a confidence threshold of 0.5, and ROI masks of the meniscus region predicted by the model were created from these images. The confidence threshold was set to 0.5 to ensure a balanced performance between P and R values. The resulting masks were used for qualitative visual analysis and evaluated in the proposed method's pipeline. Figure 6 shows examples of the masks produced due to this process. The original images, ground truth, and the prediction masks of Model 1 in the test set are also shown in Figure 6.  
The masks created using YOLO models were developed with Ensemble Voting Methods and evaluated both in the test and external sets. Several methods were tested in the study, including pixel-based voting (Method 1), weighted ensemble voting (Method 2), and dynamic weighted ensemble voting optimized by grid search (Method 3). The masks for these methods are generated by the multiple voting methods described in the Materials and Methods section. The quantitative results of the models and the proposed method on the test set and the external set are presented in Tables 5-7. Performance metrics are reported with 95% confidence intervals, and confidence intervals are denoted by the \(\pm\) symbol to  
Figure 5: Qualitative visual analysis results were obtained by the models in the test set and external set.