## 7 Experimental Results  
### Evaluation Metrics  
The number of correct predictions of healthy and TB patients was used to determine the SLDT performance. The following performance criteria were utilized to evaluate:accuracy, sensitivity, specificity, and area under curve (AUC). These variables are expressed in Equations (15)-(17) below. \[Sensitivity(\%)=TP/TP+FN\times 100 \tag{15}\]  
\[Specificity(\%)=TN/FP+TN\times 100 \tag{16}\]  
\[Accuracy(\%)=(TP+TN)/TP+FN+TN+FP\times 100 \tag{17}\]  
where _TP_, _TN_, _FN_, and _FP_ are True Positive, True Negative, False Negative, and False Positive, respectively. The results of several phases performed on the normal and abnormal images of TB CXRs were illustrated by various datasets such as MC and CHN, which are shown in Figures 3-6, respectively. Figure 3 was processed and taken from normal images of the CHN dataset. In Figure 3, the first column (a) illustrates the input image, which is an normal CXR image; the second column (b) illustrates the output image, which was equalized from the given normal input image; the third column (c) illustrates the Weiner Filtered image of the given input image; and the fourth column (d) illustrates the output image, which was segmented using ROI-based morphological operations. It can be noted that column d is segmented, which is output from the first column (a), second column (b), and third column (c). We could observe different between original images and segmented images using our proposed system. The above experiment was processed on a CHN normal dataset. Figure 4 was processed and taken from abnormal images of the CHN dataset. In Figure 4, the first column (a) illustrates the input image, which is the abnormal CXR image; the second column (b) illustrates the output image, which was equalized from the given abnormal input image; the third column (c) illustrates the Weiner Filtered image of the given input image; and the fourth column (d) illustrates the output image, which was segmented using ROI-based morphological operations. The above experimental results were processed and taken from abnormal dataset of CHN. Figure 5 was taken from normal images of the MC dataset. In Figure 5, the first column (a) illustrates the input image, which is the normal CXR image; the second column (b) illustrates the output image, which was equalized from the  
Figure 5: (a) Input Images—Normal; (b) Equalized images; (c) Weiner Filter; (d) Segmented images. Figure 6: (a) Input Images—Abnormal; (b) Equalized images; (c) Weiner Filter; (d) Segmented images. given normal input image; the third column (c) illustrates the Weiner Filtered image of the given input image; and the fourth column (d) illustrates the output image, which was segmented using ROI-based morphological operations. These images were taken from the MC normal dataset. Figure 6 was processed and taken from abnormal images of the MC dataset. In Figure 6, the first column (a) illustrates the input image, which is the abnormal CXR image; the second column (b) illustrates the output image, which was equalized from the given abnormal input image; the third column (c) illustrates the Weiner Filtered image of the given input image; and the fourth column (d) illustrates the output image, which was segmented using ROI-based morphological operations. The above experimental results were processed and taken from the abnormal dataset of MC. The performance metrics, which were evaluated using the formulas that were given above and its comparison with the existing systems with the proposed system, were displayed in Tables 1-4 with respect to the CHN, MC, combined datasets 1 (MC and CHN) and the other combined datasets 2 (NLM, Belarus, and RSNA). In Table 2, we compared the performance measures of accuracy, sensitivity, specificity, and AUC with existing methods by the same dataset such as MC. The proposed method attained the highest accuracy rate among the existing methods. Moreover, the sensitivity, specificity, and AUC scores were presented.