## 4 Experimental Results and Discussions
### Visualization and Localization Result of TB Manifestations  
Visualizations and localizations provide a'sanity check', to show where the prediction decisions are made on TB lesions or on the surrounding context of the image, and to assist in validating the model. Since only the MC dataset provides the clinical reports of TB locations, images from the MC dataset only were used to evaluate visualization and disease localization performance. OS and Grad-CAM methods were used as visualization techniques. OS and grad-CAM heat maps were computed from our method and overlaid on the image to identify the indicative regions of TB manifestation. Intense red pixels show the relevant pixels for a certain class. If the input image was classified as TB positive, intense red pixels label regions, which have a high impact on that decision and indicate the disease location. To obtain the best results using the OS map, it is vital to use the right side of the occlusion mask and stride options. Tuning their values helps provide more flexibility to investigate the input features at different length scales. From the empirical results, we set the occlusion mask size to 20% of the input square image and the stride to 10% of the input square image. In addition, we used the black occlusion mask containing zero pixel intensity instead of a gray square mask of 128 pixel intensity. This is based on the consideration that the images themselves are primarily the gray level, and occluding using a gray mask does not conceal much information compared to the neighborhood. For Grad-CAM, it required no tuning of any parameters. Several examples of visual results for the correct TB prediction and lesion localizations are given in Figure 6. It shows that our cascade algorithm correctly detected and localized common TB lesions such as pleural effusion, TB scars, infiltrate, cavity, nodular infiltrate, apical fibrosis lesion, and pleural blunting and infiltrate with calcification. As shown in Figure 7, even though the model correctly predicts TB, it does not always detect all lesions and sometimes counts too many. Frequently, it fails to identify all the lesions when there are multiple lesions. In Figure 7a, both OS and Grad-CAM methods failed to mark a pleural effusion, even though the lesion is significant. In Figure 7b, the algorithm missed locating the lesions in the right lung. Figure 7c shows the over-counting errors. Both OS and Grad-Cam not only detected the TB lesions in the lung region, but also wrongly focused on some regions outside the lung. The localization maps of OS and Grad Cam differed slightly. Grad-CAM was relatively better in pathologies localization than OS. This could be more obviously noticed when the image presents multiple lesions. The Grad-CAM computation was faster than OS, without needing parameter tuning. However, it commonly provided a lower resolution map than OS and could overlook fine details. Applying object detection networks, e.g., RetinaNet, \begin{table}
\begin{tabular}{l c c c c} \hline  & \multicolumn{2}{c}{**MC Dataset**} & \multicolumn{2}{c}{**SZ Dataset**} \\ \cline{2-5}
**Algorithm** & **ACC** & **AUC** & **ACC** & **AUC** \\ \hline [PERSON] et al. [7] & 78.3 & 86.9 & 84.0 & 90.0 \\ [PERSON] et al. [8] & 78.3 & 87.0 & 95.6 & 99.0 \\ [PERSON] et al. [9] & - & - & 85.0 & 93.4 \\ [PERSON] [10] & 68.1 & 71.0 & 83.4 & 91.0 \\ [PERSON] et al. [11] & 83.0 & 90.0 & 91.0 & 96.0 \\ [PERSON] et al. [13] & 67.4 & 88.4 & 83.7 & 92.6 \\ [PERSON] et al. [14] & 79.0 & 81.1 & 84.4 & 90.0 \\ [PERSON] et al. [15] & 82.6 & 92.6 & 84.7 & 92.6 \\ [PERSON] et al. [16] & - & - & 90.0 & 94 \\ [PERSON] et al. [18] & 87.5 & 98.6 & 95.9 & 99.4 \\ Our cascade algorithm & 92.7 & 99.0 & 95.5 & 99.7 \\ \hline \end{tabular}
\end{table} 
Table 8: Our cascade algorithm vs. previous studies (in %). could allow for explicit localization of TB abnormalities at higher resolutions. However, these networks require the manual annotation of regions of interest or explicit labeling of different TB lesions.