## 3 Results and Discussions
\begin{table}
\begin{tabular}{c l c c c c c c} \hline \hline \multirow{2}{*}{**Classifiers**} & \multirow{2}{*}{**Assessments**} & \multicolumn{6}{c}{**Five-fold Cross-Validation**} \\ \cline{3-8}  & & **Fold-1** & **Fold-2** & **Fold-3** & **Fold-4** & **Fold-5** & **Mean** \\ \hline \multirow{6}{*}{Original} & Accuracy & 0.8986 & 0.9128 & 0.8899 & 0.9263 & 0.9037 & 0.9063 \\  & Sensitivity & 0.8991 & 0.9358 & 0.9174 & 0.9444 & 0.8899 & 0.9173 \\  & Specificity & 0.8981 & 0.8899 & 0.8624 & 0.9083 & 0.9174 & 0.8952 \\ RF & False Positive & 0.1019 & 0.1101 & 0.1376 & 0.0917 & 0.0826 & 0.1048 \\  & F1-score & 0.8991 & 0.9148 & 0.8929 & 0.9273 & 0.9023 & 0.9073 \\  & AUC & 0.9413 & 0.9279 & 0.9249 & 0.9529 & 0.9383 & 0.9371 \\ \hline \multirow{6}{*}{RELIEFF + RF} & Accuracy & 0.9124 & 0.9174 & 0.9171 & 0.9358 & 0.9220 & 0.9209 \\  & Sensitivity & 0.9352 & 0.9358 & 0.9174 & 0.9541 & 0.9358 & 0.9357 \\  & Specificity & 0.8899 & 0.8991 & 0.9167 & 0.9174 & 0.9083 & 0.9063 \\  & False Positive & 0.1101 & 0.1009 & 0.0833 & 0.0826 & 0.0917 & 0.0937 \\  & F1-score & 0.9140 & 0.9189 & 0.9174 & 0.9369 & 0.9231 & 0.9221 \\  & AUC & 0.9760 & 0.9632 & 0.9805 & 0.9786 & 0.9822 & 0.9761 \\ \hline \multirow{6}{*}{GA + RF} & Accuracy & 0.9174 & 0.9174 & 0.9124 & 0.9263 & 0.8945 & 0.9136 \\  & Sensitivity & 0.9725 & 0.9725 & 0.9444 & 0.9358 & 0.9174 & 0.9485 \\  & Specificity & 0.8624 & 0.8624 & 0.8807 & 0.9167 & 0.8716 & 0.8787 \\  & False Positive & 0.1376 & 0.1376 & 0.1193 & 0.0833 & 0.1284 & 0.1213 \\  & F1-score & 0.9217 & 0.9217 & 0.9148 & 0.9273 & 0.8969 & 0.9165 \\  & AUC & 0.9646 & 0.9690 & 0.9822 & 0.9761 & 0.9729 & 0.9730 \\ \hline \multirow{6}{*}{PSO + RF} & Accuracy & 0.8853 & 0.9312 & 0.9174 & 0.8986 & 0.9217 & 0.9108 \\  & Sensitivity & 0.9266 & 0.9633 & 0.9633 & 0.9266 & 0.9259 & 0.9411 \\  & Specificity & 0.8440 & 0.8991 & 0.8716 & 0.8704 & 0.9174 & 0.8805 \\  & False Positive & 0.1560 & 0.1009 & 0.1284 & 0.1296 & 0.0826 & 0.1195 \\  & F1-score & 0.8899 & 0.9333 & 0.9211 & 0.9018 & 0.9217 & 0.9135 \\  & AUC & 0.9532 & 0.9736 & 0.9697 & 0.9397 & 0.9718 & 0.9616 \\ \hline \multirow{6}{*}{FA + RF} & Accuracy & 0.9078 & 0.9539 & 0.9266 & 0.9404 & 0.9266 & 0.9311 \\  & Sensitivity & 0.9630 & 0.9633 & 0.9174 & 0.9817 & 0.9174 & 0.9486 \\ \cline{1-1}  & Specificity & 0.8532 & 0.9444 & 0.9358 & 0.8991 & 0.9358 & 0.9137 \\ \cline{1-1}  & False Positive & 0.1468 & 0.0556 & 0.0642 & 0.1009 & 0.0642 & 0.0863 \\ \cline{1-1}  & F1-score & 0.9123 & 0.9545 & 0.9259 & 0.9427 & 0.9259 & 0.9323 \\ \cline{1-1}  & AUC & 0.9862 & 0.9859 & 0.9838 & 0.9826 & 0.9649 & 0.9807 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance assessment measurements of original RF and four improved RFs with respect to five-fold cross-validation. the detection of juxta-pleural nodules, regardless of the size, which is beneficial to the use of the 3D chain coding method for border reconstruction. Figure 13: Results of the proposed nodule detection. (**a**) Solid isolated nodule, (**b**) isolated ground glass nodule, (**c**) small juxta-pleural nodule, (**d**) big juxta-pleural nodule, and (**e**) juxta-vascular nodule. Lastly, Figure 13e shows the detection of a juxta-vascular nodule, which is also challenging to detect due to nearby pulmonary structures. Our method can successfully detect them with the help of the blob enhancement filter in preliminary screening. Therefore, it is evident that our proposed method can detect any variants of nodules. Moreover, we compute execution time for our proposed nodule detection because it is an essential factor in evaluating the performance of an algorithm. Table 4 describes the profile summary, which shows the time complexity for each function to detect the pulmonary nodules from a single exam with a maximum number of slices. From this table, we can see that our automated nodule detection takes 144.4 seconds (2.4 minutes) in maximum to predict a single CT exam on a standard processor with Intel(R) Core (TM) i7-6500U CPU @2.6 GHz.