## 4 Experimental Results and Discussions
### TB Prediction Using Ensemble Model  
The MC and SZ datasets were used for the prediction task. All the images were segmented using DLabv3+X and cropped to a bounding box to contain all lung pixels and used as input to the prediction. Twelve DCNNs are trained using the SGDM optimizer with five-fold cross-validation. Due to limited GPU memory, the batch size was set to 4. Since SGDM parameters can strongly affect individual DCNN performance, it is important to select the appropriate values for them. The common parameters for training the DCNN model with SGDM are learning rate, momentum, and L2-regularization's decay rate. The proper tuning of these parameters can help the DCNN model become more robust and reliable. Therefore, we initially defined the search space: learning rate, \(\alpha\in(0.01,1)\), momentum, \(\gamma\in(0.8,0.99)\), and L2-regularization's decay rate, \(\lambda\in(0,1)\), and searched for optimal values using a Bayesian algorithm. The optimal parameters for each DCNN are listed in Table 6. Once each DCNN model learned the optimal parameters, it was combined with the ensemble classifier. Combining all DCNN models consumed large amounts of computation time yet did not improve the overall result, so we attempted to form the best combination of a smaller set of DCNN models. We tested different combinations of trained DCNNs, measured their accuracies, and picked the combination that provided the highest accuracy. The empirical experiments showed that a combination of DenseNet, ResNet-50, SqueezeNet, and MobileNet led to stable and promising results for both datasets. Thus, we built an ensemble classifier of those four DCNNs by averaging their probabilities. The performance of the ensemble classifier was assessed with three metrics: F-measure (F1), accuracy (ACC), and area under curve (AUC). Using _TP_, _TN_, _FN_, _FP_, F1, and ACC were computed as  
\[F-measure\left(F1\right)=2\frac{Recall\times Precision}{Recall+Precision} \tag{8}\]  
\[accuracy\left(ACC\right)=\frac{TP+TN}{TP+TN+FP+FN} \tag{9}\]  
where TP is the number of images correctly predicted as TB, TN is the number of correctly predicted normal images, FP is the number of images wrongly predicted as positive for \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Dataset** & **IoU (\%)** & **DSC (\%)** & **Accuracy (\%)** \\ \hline
[28] & JSRT & \(94.6\pm 1.8\) & - & - \\
[29] & private dataset & \(88.0\pm 7.0\) & - & - \\
[30] & private dataset & \(80.9\) & - & - \\
[31] & JSRT & \(95.2\pm 1.8\) & \(97.5\pm 1.0\) & - \\
[32] & JSRT & \(95.3\pm 2.0\) & - & - \\
[33] & JSRT & \(95.2\pm 1.9\) & - & - \\
[34] & JSRT & \(93.0\pm 1.8\) & \(96.4\pm 1.0\) & \(97.6\pm 0.6\) \\
[35] & JSRT & \(85.4\pm 4.9\) & - & - \\
[36] & JSRT & \(86.9\pm 5.9\) & - & \(95.7\pm 2.4\) \\
[37] & JSRT & \(94.5\) & - & \(98.2\) \\
[38] & JSRT & \(95.4\pm 1.5\) & \(9.67\pm 0.8\) & - \\ MC & \(94.1\pm 3.4\) & \(96.0\pm 1.8\) & - \\
[39] & JSRT & \(95.0\) & \(97.4\) & - \\
[40] & JSRT + MC & \(95.1\) & - & \(98.7\) \\
[41] & MC & \(93.1\pm 4.9\) & \(96.4\pm 2.8\) & - \\  & JSRT & \(96.1\pm 0.8\) & \(98.0\pm 0.4\) & \(98.8\pm 0.4\) \\ Our DLabv3+X & MC & \(95.1\pm 1.5\) & \(97.4\pm 0.8\) & \(98.7\pm 0.3\) \\  & SZ & \(92.8\pm 4.2\) & \(96.2\pm 2.4\) & \(97.9\pm 1.5\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: DLabv3+ X lung segmentation compared to previous studies. TB, and FN is the wrongly predicted normal images. Recall is TP/TP + FN and precision is TP/TP + FP, respectively. AUC is an important measure for comparing classifiers; it is computed from the receiver operating characteristics curve. Our ensemble classifier results are in Table 7, where they are compared with those for the individual DCNNs. Among individual DCNNs, MobileNet and DenseNet made better predictions than the others. The ensemble of DenseNet, ResNet-50, SqueezeNet, and MobileNet performed better than individual DCNNs. It achieved an accuracy of 92.7 for MC and an accuracy of 95.5 for SZ, significantly better than the accuracy of individual DCNNs.