## 5 Conclusions  
We present a technique for learning with a hybrid of hand-crafted features and deep-activated features from pre-trained CNNs, with help of a PSO algorithm and an optimized SVM classifier. We initially preprocessed the images using CLAHE and subsequently segmented the lung regions using a deep semantic segmentation technique, which used XceptionNet as the backbone in DeepLabv3+. From the segmented lung regions, we extracted six sets of hand-crafted features using statistical textures, LBP, GIST, HOG, PHOG, and BoVW feature descriptors. Then, we used nine pre-trained CNNs as the feature descriptors to retrieve the deep-activated features. To select the important features from each feature set, a PSO based feature selection algorithm selected different fractions of features. \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline \multirow{2}{*}{**Authors**} & \multirow{2}{*}{**Methods Used**} & \multicolumn{2}{c}{**MC**} & \multicolumn{2}{c}{**Shenzhen**} \\ \cline{3-8}  & & **Accuracy** & **AUC** & **Accuracy** & **AUC** \\ \hline \multirow{4}{*}{[PERSON] et al. [7]} & \multirow{4}{*}{Lung segmentation using optimized graph cuts} & \multirow{4}{*}{78.3\%} & \multirow{4}{*}{86.9\%} & \multirow{4}{*}{84.0\%} & \multirow{4}{*}{90.0\%} \\  & & Multiple local and global hand-crafted features & & & & \\  & & SVM classifier & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [8]} & \multirow{4}{*}{Lung segmentation using optimized graph cuts} & \multirow{4}{*}{Multiple local and global hand-crafted features} & \multirow{4}{*}{78.3\%} & \multirow{4}{*}{86.9\%} & \multirow{4}{*}{84.0\%} & \multirow{4}{*}{90.0\%} \\  & & SVM classifier & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [8]} & \multirow{4}{*}{Lung segmentation using thresholding} & \multirow{4}{*}{78.3\%} & \multirow{4}{*}{76.0\%} & \multirow{4}{*}{95.6\%} & \multirow{4}{*}{99.0\%} \\  & & SVM classifier & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [9]} & \multirow{4}{*}{Lung segmentation using optimized graph cuts} & \multirow{4}{*}{71.0\%} & \multirow{4}{*}{83.4\%} & \multirow{4}{*}{91.0\%} \\  & & SVM & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [11]} & \multirow{4}{*}{Lung segmentation using thresholding} & \multirow{4}{*}{68.1\%} & \multirow{4}{*}{71.0\%} & \multirow{4}{*}{83.4\%} & \multirow{4}{*}{96.0\%} \\  & & SVM classifier & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [17]} & \multirow{4}{*}{Lung segmentation using threshold} & \multirow{4}{*}{67.0\%} & \multirow{4}{*}{81.1\%} & \multirow{4}{*}{84.4\%} & \multirow{4}{*}{90.0\%} \\  & & SVM & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [18]} & \multirow{4}{*}{Lung segmentation using threshold} & \multirow{4}{*}{79.0\%} & \multirow{4}{*}{88.4\%} & \multirow{4}{*}{83.7\%} & \multirow{4}{*}{92.6\%} \\  & & A combination of a Bayes network, MLP and random forest & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [17]} & \multirow{4}{*}{Lung segmentation using threshold} & \multirow{4}{*}{79.0\%} & \multirow{4}{*}{81.1\%} & \multirow{4}{*}{84.4\%} & \multirow{4}{*}{90.0\%} \\  & & A variant of AlexNet architecture & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [18]} & \multirow{4}{*}{Lung segmentation using threshold} & \multirow{4}{*}{76.4\%} & \multirow{4}{*}{88.4\%} & \multirow{4}{*}{83.7\%} & \multirow{4}{*}{92.6\%} \\  & & Pre-trained AlexNet & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [19]} & \multirow{4}{*}{Lung segmentation using threshold} & \multirow{4}{*}{82.6\%} & \multirow{4}{*}{92.6\%} & \multirow{4}{*}{84.7\%} & \multirow{4}{*}{92.6\%} \\  & & Ensemble of pre-trained CNNs & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [21]} & \multirow{4}{*}{Lung segmentation using threshold} & \multirow{4}{*}{82.6\%} & \multirow{4}{*}{92.6\%} & \multirow{4}{*}{84.7\%} & \multirow{4}{*}{92.6\%} \\  & & and VGGNet & & & & \\ \cline{1-1}  & & SVM & & & \\ \hline \multirow{4}{*}{[PERSON] et al.