## 4 Experimental Validation
Figure 7 reports the precision-recall curve analysis results accomplished by the CSO-CADLCC model under distinct TR/TS data. The figures indicate that the proposed CSO-CADLCC model produced effectual outcomes under distinct TR/TS data. Table 2 reports extensive lung cancer classification outcomes achieved by the proposed CSO-CADLCC model and other existing models [23; 24]. Figure 8 compares the \(acu_{y}\) values achieved by the CSO-CADLCC model and other recent models. The figure indicates that the AlexNet model achieved poor results with low \(acu_{y}\). At the same time, the ResNet5-SVM and AlexNet models depicted slightly improved \(acu_{y}\) values. Furthermore, the optimal-DNN, artificial neural network (ANN), multi-class CNN (MC-CNN), CNN, and visual geometry group (VGG16) models demonstrated reasonable \(acu_{y}\) values. However, the proposed CSO-CADLCC model outperformed all other methods, and achieved a maximum \(acu_{y}\) of 98.89%. Figure 9 portrays the comparative analysis results in terms of \(prec_{n}\), \(rec_{l}\), \(spec_{y}\), and \(F_{score}\) achieved by the CSO-CADLCC approach against existing methods. The figure demonstrates that the AlexNet approach achieved the least outcomes, with lesser values of \(prec_{n}\), \(rec_{l}\), \(spec_{y}\), and \(F_{score}\). Moreover, the ResNet5-SVM and AlexNet techniques demonstrated somewhat enhanced values of \(prec_{n}\), \(rec_{l}\), \(spec_{y}\), and \(F_{score}\). Furthermore, the optimal-DNN, ANN, MC-CNN, CNN, and VGG16 techniques have accomplished reasonable values of \(prec_{n}\), \(rec_{l}\), \(spec_{y}\), and \(F_{score}\). Eventually, the proposed CSO-CADLCC approach outperformed all other approaches, with maximal \(prec_{n}\), \(rec_{l}\), \(spec_{y}\), and \(F_{score}\) values of 98.41%, 98.25%, 99.17%, and 98.29%, respectively. Figure 7: Precision–recall analysis results of the CSO-CADLCC technique under distinct TR/TS data. (**a**) TR/TS data of 90:10, (**b**) TR/TS data of 80:20, (**c**) TR/TS data of 70:30, and (**d**) TR/TS data of 60:40. Figure 8: Accuracy analysis of the CSO-CADLCC technique with recent algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F-Score** \\ \hline Optimal-DNN & 97.81 & 97.73 & 97.68 & 97.54 & 95.15 \\ \hline Artificial NN & 97.17 & 95.02 & 95.72 & 96.29 & 96.46 \\ \hline MC-CNN & 97.78 & 95.43 & 97.72 & 97.58 & 95.77 \\ \hline CNN & 97.87 & 96.92 & 95.26 & 96.61 & 95.73 \\ \hline ResNet50-SVM & 96.30 & 97.67 & 96.68 & 96.54 & 96.60 \\ \hline AlexNet & 95.50 & 97.36 & 97.34 & 95.83 & 95.65 \\ \hline VGG16 & 97.99 & 95.30 & 96.48 & 97.11 & 97.26 \\ \hline CSO-CADLCC & 98.89 & 98.41 & 98.25 & 99.17 & 98.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis between the CSO-CADLCC technique and other recent algorithms. Figure 9: Comparative analysis of the CSO-CADLCC technique with recent algorithms. A detailed running time (RT) and training time (TT) analysis was conducted between the CSO-CADLCC model and recent models, and the results are shown in Table 3 and Figure 10. The results imply that the optimal DNN model offered a poor performance with a maximum RT of 10.55 s. At the same time, the ANN, MC-CNN, CNN, and ResNet50-SVM models obtained slightly decreased RT values of 9.97 s, 9.13 s, 8.66 s, and 7.99 s, respectively. Moreover, the AlexNet and VGG16 models resulted in reasonable RT values of 5.92 s and 3.38 s, respectively. However, the proposed CSO-CADLCC model accomplished the maximum performance by achieving the lowest RT of 1.68 s. At the same time, it can also be noted that the proposed model underwent the lowest training time of 21.54 min, whereas the optimal DNN model underwent the maximum training time of 58.45 min. Based on the aforementioned results and discussion, it can be inferred that the CSO-CADLCC methodology achieved the maximum performance in lung cancer classification.