## 4 Experimental Results
### Classification Results
#### 4.2.2 Bagging Ensemble Classification Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Bagging Ensemble** & **Model Name** & **Model Numbers** & **Accuracy** & **F1-Score** & **AUC** & **PPV** & **NPV** & **Sensitivity** & **Specificity** \\ \hline ResNet-18 & 3 & 0.62 & 0.56 & 0.62 & 0.76 & 0.57 & 0.51 & 0.73 \\ \hline ResNet-34 & 3 & 0.65 & 0.61 & 0.65 & 0.78 & 0.62 & 0.57 & 0.73 \\ \hline DenseNet-121 & 3 & 0.62 & 0.60 & 0.62 & 0.67 & 0.61 & 0.59 & 0.64 \\ \hline ResNet-18+ & & & & & & & & \\ DenseNet-169+ & 3 & 0.67 & 0.61 & 0.67 & 0.83 & 0.64 & 0.55 & 0.79 \\ MobileNet-V2 & & & & & & & & \\ \hline ResNet-34+ & & & & & & & & \\ ResNet-18+ & 3 & 0.67 & 0.60 & 0.67 & 0.78 & 0.65 & 0.54 & 0.80 \\ ResNet-18 & & & & & & & & \\ \hline MobileNet,V2+ & & & & & & & & \\ DenseNet-121+ & 3 & 0.66 & 0.62 & 0.66 & 0.77 & 0.66 & 0.58 & 0.74 \\ DenseNet-169 & & & & & & & & \\ \hline ResNet-34+ & & & & & & & & \\ ResNet-18+ & & & & & & & & \\ MobileNet-V2 & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: This table shows the bagging ensemble result of all folds datasets with majority voting fusion methods. The comparison results between the bagging ensemble with three fusion methods and all baseline results are displayed in Table 8. Three fusion methods were utilized in the bagging ensemble experiment. The best bagging ensemble result was achieved using the performance weighting fusion method: 0.70 accuracy, 0.63 F1-Score, 0.75 AUC, 0.84 PPV, 0.68 NPV, 0.56 sensitivity, and 0.85 specificity. Compared with the best baseline experimental results, the proposed CAD system using the bagging and down-sampling technique improved the seven parameters as follows: accuracy 7%, F1-score 7%, AUC 13%, PPV 8%, NPV 9%, sensitivity 6%, and specificity 7%. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Bagging Ensemble** & **Model Name** & **Model Numbers** & **Accuracy** & **F1-Score** & **AUC** & **PPV** & **NPV** & **Sensitivity** & **Specificity** \\ \hline \multirow{4}{*}{Output Average} & ResNet-18 & 2 & 0.70 & 0.63 & 0.70 & 0.87 & 0.67 & 0.56 & 0.84 \\ \cline{2-9}  & ResNet-34 & 3 & 0.65 & 0.61 & 0.65 & 0.79 & 0.63 & 0.58 & 0.73 \\ \cline{2-9}  & DenseNet-121 & 2 & 0.66 & 0.60 & 0.66 & 0.84 & 0.70 & 0.54 & 0.78 \\ \hline \multirow{4}{*}{Output Average} & MobileNet-V2 & 3 & 0.64 & 0.57 & 0.64 & 0.75 & 0.61 & 0.51 & 0.77 \\ \cline{2-9}  & ResNet-18+ & & & & & & & \\ \cline{2-9}  & DenseNet-169+ & 3 & 0.67 & 0.61 & 0.67 & 0.84 & 0.64 & 0.55 & 0.80 \\ \cline{2-9}  & MobileNet-V2 & & & & & & & & \\ \cline{2-9}  & ResNet-34+ & & & & & & & & \\ \cline{2-9} Output Average & ResNet-34+ & & & & & & & & \\ \cline{2-9}  & ResNet-18+ & 3 & 0.68 & 0.60 & 0.68 & 0.81 & 0.66 & 0.53 & 0.84 \\ \cline{2-9}  & ResNet-121+ & 3 & 0.65 & 0.59 & 0.65 & 0.77 & 0.64 & 0.55 & 0.75 \\ \cline{2-9}  & DenseNet-169 & & & & & & & & \\ \cline{2-9}  & ResNet-34+ & & & & & & & & \\ \cline{2-9}  & ResNet-18+ & 3 & 0.70 & 0.63 & 0.70 & 0.84 & 0.68 & 0.57 & 0.84 \\ \hline \end{tabular}
\end{table} 
Table 6: This table shows the bagging ensemble result of all folds datasets with output average fusion methods.