## 4 Experimental Results and Discussion
The performance of the SVM classifier using the hybrid feature set is in Table 4 for MC and Table 5 for Shenzen datasets. Its Kappa indices are plotted in Figures 12 and 13. It achieved favorable performance with 93.3% F1, 92.7% accuracy, 99.5% AUC for MC and 95.4% F1, 95.5% accuracy, 99.5% AUC for Shenzhen. Its Kappa was 'excellent' for both datasets. The hybrid feature set marginally improved the prediction compared to individual best-performing feature sets on Shenzen dataset while matching the best prediction made by HOG and DenseNet feature sets using the MC dataset. Figure 11: Selected deep-activated features using PSO vs. fraction of features selected. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Features** & **F1 (\%)** & **Accuracy (\%)** & **AUC (\%)** \\ \hline Statistical Textures & 81.8 & 80.5 & 87.9 \\ \hline LBP & 79.2 & 73.2 & 86.2 \\ \hline GIST & 90.9 & 90.2 & 93.1 \\ \hline HOG & 93.3 & 92.7 & 100.0 \\ \hline PHOG & 75.6 & 73.2 & 83.6 \\ \hline BoVW & 91.3 & 90.2 & 99.8 \\ \hline AlexNet & 76.9 & 70.7 & 85.7 \\ \hline GoogLeNet & 88.9 & 87.8 & 91.9 \\ \hline InceptionV3 & 83.7 & 82.9 & 89.0 \\ \hline XceptionNet & 85.7 & 82.9 & 91.0 \\ \hline ResNet-50 & 81.8 & 80.5 & 88.8 \\ \hline SqueezeNet & 81.6 & 78.0 & 79.5 \\ \hline ShuffleNet & 83.3 & 80.5 & 84.0 \\ \hline MobileNet & 90.9 & 90.2 & 93.1 \\ \hline DenseNet & 93.3 & 92.7 & 99.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of each individual set and hybrid feature using the optimized SVM classifiers—MC dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Feature Sets** & **Penalty Term (C)** & **Kernel Functions** & **Kernel Scale (\(\boldsymbol{y}\))** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **Fault  
We compared our method with previous studies in Table 6. Our method surpassed the existing studies for the MC dataset with accuracy 92.7% and AUC 99.5%, and obtained comparable results with the state of the art for the Shenzen dataset with accuracy 95.9% and AUC 99.5%. From Table 6, we found that the methods produced better performance on Shenzhen dataset compared to MC dataset. The same pattern is seen Tables 4 and 5 of our study, and also in related works [7,8,10,11,17,18,21,22]. The lower performance with the smaller MC dataset is probably attributed to its limited size containing only 138 x-rays, and therefore lower range of samples to be trained and learnt. Shenzhen dataset is larger than MC dataset where the number of samples is over 300, both for normal and TB cases. Another factor of impairing the classification accuracy could be the unbalanced data distribution. The MC dataset is unbalanced, with a smaller number of TB cases while the Shenzen set is larger and balanced with almost 50% TB cases. It is also noteworthy that the most hand-crafted used here have already been studied. However, they were directly input to the classifier. On the other hand, our method, first filtered out the noisy and irrelevant features and selected the dominant features. For deep-activated features, few pre-trained CNNs were used in previous studies, whereas we studied a wide variety of different CNN structures. We also first selected the important deep activated features, before feeding them to the classifier. [PERSON] et al. [22] used hand-crafted based classifiers and pre-trained CNNs separately and, combined their results via majority voting to make a final decision. Here, we made alternative use of these features. Our method first selected the important features and hybridized the best-performing shallow hand-crafted features and high-level deep features, so that the classifier had better information to make the prediction. Achieving the better results than the previous studies using the same feature sets was due to the feature selection. Using a PSO feature selection and hybridizing the hand-crafted and deep-activated features were the keys to improved prediction. The code supporting the findings of this study are available from the corresponding authors upon request.