## 4 Experimental Results and Discussions
### Lung Segmentation
In the severe cases of a cavitary infiltrate, or pleural effusion, the shape and pixel intensity of the lung significantly differ from those of a normal lung. These TB abnormalities can cause problems for segmentation methods, which are modeled on normal lungs only. Therefore, we coded and tested FCN, two variants of SegNet based on VGG16 and VGG17 (labeled Seg-VGG16 and Seg-VGG19), and classical U-Net on the MC and SZ datasets, where many images contain TB pathologies. The same parameter values of our DLabv3+X were used to train these models. Their results are tabulated and compared with DeepLabV3+ based models in Table 2 for MC and Table 3 for SZ. To enable direct comparison with existing studies, we also tested DLabv3+X along with DLabv3+R18, DLabv3+R50, and DLabv3+M on the JSRT dataset. The ground truth masks for the JSRT dataset were obtained from [PERSON] et al. (2018). Following the same partition ratio of existing studies, the JSRT dataset was split into 50% training and 50% testing. JSRT results are tabulated in Table 4. Tables 2-4 clearly show that DLabv3+ based models outperformed FCN, SegNet-based methods, and U-Net for all evaluated datasets. Among DLabV3+ based models, our DLabv3+X provided the highest segmentation performance with IoUs of 95.1 \(\pm\) 1.5% for MC, 92.8 \(\pm\) 4.2% for SZ, and 96.1\(\pm\)0.8% for JSRT. For the MC dataset, DLabv3+R18 also yielded comparable results to DLabv3+X by giving an IoU of 94.4 \(\pm\) 1.5%. For the SZ dataset, DLabv3+R18 and DLabv3+R50 obtained the comparable IoUs of 92.4 \(\pm\) 4.8% and 92.3 \(\pm\) 4.7% to 92.8 \(\pm\) 4.2% of DLabv3+X. For the JSRT dataset, DLabv3+R18, DLabv3+R50, and DLabv3+M obtained similar IoUs of 94.7 \(\pm\) 2.0%, 94.5 \(\pm\) 1.9, and 94.9 \(\pm\) 1.8%, respectively, while DLabv3+X achieved the highest IoU of 96.1 \(\pm\) 0.8%. In Table 5, we further compared our segmentation method with extant lung segmentation algorithms and showed that it surpassed existing studies at IoUs 95.1 \(\pm\) 1.5 for MC and 92.8 \(\pm\) 4.2 for SZ. For validation, using JSRT, our IoU, at 96.1 \(\pm\) 0.8, matched [PERSON] and [PERSON] (2018), but surpassed others. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithms** & **IoU (\%)** & **DSC (\%)** & **Accuracy (\%)** \\ \hline FCN & 86.3 \(\pm\) 4.2 & 92.6 \(\pm\) 2.5 & 96.2 \(\pm\) 1.6 \\ Seg-VGG16 & 89.0 \(\pm\) 4.0 & 94.1 \(\pm\) 2.3 & 96.8 \(\pm\) 1.1 \\ Seg-VGG19 & 87.9 \(\pm\) 3.3 & 93.6 \(\pm\) 1.9 & 96.5 \(\pm\) 0.9 \\ U-Net & 90.6 \(\pm\) 5.5 & 94.9 \(\pm\) 3.2 & 97.4 \(\pm\) 1.4 \\ DLabv3 + R18 & 94.4 \(\pm\)1.5 & 97.1 \(\pm\) 0.8 & 98.5 \(\pm\) 0.3 \\ DLabv3 + R50 & 93.6 \(\pm\) 1.6 & 96.7 \(\pm\) 0.8 & 98.3 \(\pm\) 0.5 \\ DLabv3 + M & 92.4 \(\pm\) 3.2 & 96.0 \(\pm\) 1.7 & 97.9 \(\pm\) 1.1 \\ DLabv3 + X & 95.1 \(\pm\) 1.5 & 97.5 \(\pm\) 0.8 & 98.7 \(\pm\) 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of deep semantic lung segmentation methods on MC dataset. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithms** & **IoU (\%)** & **DSC (\%)** & **Accuracy (\%)** \\ \hline FCN & 85.8 \(\pm\) 4.2 & 92.3 \(\pm\) 2.5 & 95.9 \(\pm\) 1.6 \\ Seg-VGG16 & 86.2 \(\pm\) 4.0 & 92.5 \(\pm\) 2.4 & 95.9 \(\pm\) 1.4 \\ Seg-VGG19 & 85.7 \(\pm\) 3.7 & 92.3 \(\pm\) 2.1 & 95.7 \(\pm\) 1.4 \\ U-Net & 84.8 \(\pm\) 9.1  
Figure 5: Lung segmentation using DLabv3+X (**a**) ground truth regions superimposed on original images; (**b**) predicted regions superimposed on original images; (**c**) overlap between ground truth and segmented mask.