## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **DSC** & **PPV** & **Specificity** & **Accuracy** & **Sensitivity** \\ \hline \multicolumn{4}{c}{**Test Set (Internal)**} \\ \hline Model 1 & 0.8720 (\(\pm\)0.0088) & 0.8069 (\(\pm\)0.0145) & 0.9991 (\(\pm\)0.0001) & 0.9989 (\(\pm\)0.0001) & **0.9538 (\(\pm\)0.0089)** \\ \hline Model 2 & 0.7650 (\(\pm\)0.0513) & 0.7524 (\(\pm\)0.0473) & 0.9992 (\(\pm\)0.0001) & 0.9983 (\(\pm\)0.0004) & 0.8083 (\(\pm\)0.0605) \\ \hline Model 3 & 0.8453 (\(\pm\)0.0189) & 0.8090 (\(\pm\)0.0150) & 0.9992 (\(\pm\)0.0000) & 0.9987 (\(\pm\)0.0002) & 0.9068 (\(\pm\)0.0294) \\ \hline Model 4 & 0.8369 (\(\pm\)0.0268) & 0.8028 (\(\pm\)0.0239) & 0.9992 (\(\pm\)0.0001) & 0.9987 (\(\pm\)0.0002) & 0.8910 (\(\pm\)0.0347) \\ \hline Model 5 & 0.7860 (\(\pm\)0.0397) & 0.7701 (\(\pm\)0.0356) & 0.9992 (\(\pm\)0.0001) & 0.9984 (\(\pm\)0.0002) & 0.8354 (\(\pm\)0.0518) \\ \hline Method 1 & 0.8583 (\(\pm\)0.0181) & 0.8286 (\(\pm\)0.0151) & 0.9993 (\(\pm\)0.0000) & 0.9988 (\(\pm\)0.0002) & 0.9068 (\(\pm\)0.0276) \\ \hline Method 2 & 0.8743 (\(\pm\)0.0153) & 0.8525 (\(\pm\)0.0136) & **0.9994 (\(\pm\)0.0001)** & 0.9990 (\(\pm\)0.0001) & 0.9092 (\(\pm\)0.0223) \\ \hline Method 3 & **0.8976 (\(\pm\)0.0071)** & **0.8561 (\(\pm\)0.0121)** & **0.9994 (\(\pm\)0.0000)** & **0.9992 (\(\pm\)0.0000)** & 0.9467 (\(\pm\)0.0077) \\ \hline \multicolumn{4}{c}{**External set**} \\ \hline Model 1 & 0.8852 (\(\pm\)0.0072) & 0.8643 (\(\pm\)0.0148) & 0.9993 (\(\pm\)0.0001) & 0.9988 (\(\pm\)0.0001) & 0.9159 (\(\pm\)0.0143) \\ \hline Model 2 & 0.7642 (\(\pm\)0.0570) & 0.7714 (\(\pm\)0.0562) & 0.9994 (\(\pm\)0.0000) & 0.9981 (\(\pm\)0.0004) & 0.7812 (\(\pm\)0.0634) \\ \hline Model 3 & 0.8585 (\(\pm\)0.0243) & 0.8406 (\(\pm\)0.0255) & 0.9992 (\(\pm\)0.0001) & 0.9986 (\(\pm\)0.0002) & 0.8916 (\(\pm\)0.0299) \\ \hline Model 4 & 0.8260 (\(\pm\)0.0317) & 0.8450 (\(\pm\)0.0269) & 0.9993 (\(\pm\)0.0001) & 0.9984 (\(\pm\)0.0002) & 0.8418 (\(\pm\)0.0430) \\ \hline Model 5 & 0.7800 (\(\pm\)0.0448) & 0.8098 (\(\pm\)0.0427) & 0.9993 (\(\pm\)0.0001) & 0.9981 (\(\pm\)0.0003) & 0.7842 (\(\pm\)0.0544) \\ \hline Method 1 & 0.8574 (\(\pm\)0.0254) & 0.8576 (\(\pm\)0.0257) & 0.9994 (\(\pm\)0.0000) & 0.9986 (\(\pm\)0.0002) & 0.8753 (\(\pm\)0.0331) \\ \hline Method 2 & 0.8852 (\(\pm\)0.0082) & 0.8843 (\(\pm\)0.0145) & 0.9994 (\(\pm\)0.0001) & 0.9988 (\(\pm\)0.0002) & 0.8966 (\(\pm\)0.0174) \\ \hline Method 3 & **0.9004 (\(\pm\)0.0064)** & **0.8876 (\(\pm\)0.0134)** & **0.9995 (\(\pm\)0.0000)** & **0.9990 (\(\pm\)0.0001)** & **0.9200 (\(\pm\)0.0119)** \\ \hline \end{tabular}
\end{table} 
Table 5: Qualitative evaluation results of the masks created using YOLO models and multiple voting methods on the test set and external set. As seen in Table 5, Method 3 shows successful results both on the test set and external set, in short, on all metric values. These results show that Method 3 succeeded in both segmentation accuracy and detecting positive samples correctly. In the external set, Method 3 achieved the highest value of 0.9004 (\(\pm\)0.0064) for the DSC metric, followed by 0.8976 (\(\pm\)0.0071) in the test set. Regarding PPV, Method 3 also stood out with values of 0.8561 (\(\pm\)0.0121) and 0.8876 (\(\pm\)0.0134) in both test and external sets, respectively. This shows that Method 3 performs strongly in maintaining the accuracy of optimistic predictions. Similar success was observed regarding Specificity and accuracy values, with very high values of