## 4 Experimental Validation  
The proposed model was simulated using Python 3.6.5 tool with additional packages, such as tensorflow (GPU-CUDA Enabled), keras, numpy, pickle, matplotlib, sklearn, pillow,and opencv-python. The simulation process was conducted on a PC with configurations, such as MSI Z370 A-Pro, i5-8600k, NVIDIA TITAN X, 16 GB RAM, 250 GB SSD, and 1 TB HDD. The parameter settings are given as follows: batch size: 64, number of epochs: 15, learning rate: 0.05, dropout rate: 0.25, and activation function: rectified linear unit (ReLU). The experimental validation of the proposed CSO-CADLCC model was conducted upon a benchmark lung database [22] that comprises a total of 300 images. In total, three class labels exist in the database, namely normal, benign, and malignant. Some sample images are depicted in Figure 3. The number of images under each class is 100. Figure 4 demonstrates the confusion matrices generated by the CSO-CADLCC model on the distinct sizes of training/testing (TR/TS) data. With TR/TS data of 90:10, the proposed CSO-CADLCC model categorized 7 images as normal, 8 images as malignant, and 14 images as benign. Moreover, with TR/TS data of 80:20, the CSO-CADLCC technique categorized 18 images as normal, 20 images as malignant, and 21 images as benign. Furthermore, with TR/TS data of 70:30, the presented CSO-CADLCC approach categorized 20 images as normal class, 28 images as malignant class, and 39 images as benign. Finally, with TR/TS data of 60:40, the proposed CSO-CADLCC system categorized 35 images as normal, 34 images as malignant, and 34 images as benign. Figure 3: Sample Images. Table 1 and Figure 5 illustrate a brief overview of lung cancer classification results produced by the CSO-CADLCC model under distinct classes and TR/TS data. The experimental outcomes imply that the CSO-CADLCC model accomplished a maximum performance under all TR/TS data ratios. For instance, with 90:10 TR/TS data ratio, the proposed CSO-CADLCC model achieved an average \(accu_{y}\), \(prec_{n}\), \(rec_{t}\), \(prec_{y}\), \(F_{score}\), and MCC of 97.78%, 95.83%, 96.30%, 98.55%, 95.82%, and, 94.53% respectively. Moreover, with  
Figure 4: Confusion matrix of the CSO-CADLCC technique on distinct sizes of training/testing dataset. (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. TR/TS data of 80:20, the CSO-CADLCC approach achieved an average \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), \(spec_{y}\), \(F_{score}\), and MCC of 98.89%, 98.41%, 98.25%, 99.17%, 98.29% and, 97.51%, respectively. Furthermore, with TR/TS data of 70:30, the proposed CSO-CADLCC approach accomplished an average \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), \(spec_{y}\), \(F_{score}\), and MCC of 97.78%, 96.94%, 95.65%,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F-Score** & **MCC** \\ \hline
**Training/Testing (90:10)** & & & & & & \\ \hline Normal & 96.67 & 87.50 & 100.00 & 95.65 & 93.33 & 91.49 \\ \hline Malignant & 96.67 & 100.00 & 88.89 & 100.00 & 94.12 & 92.1  
98.27%, 96.10%, and, 94.68% respectively. In addition, with TR/TS data of 60:40, the presented CSO-CADLCC technique obtained an average \(accu_{y}\), \(prec_{n}\), \(rec_{l}\), \(spec_{y}\), \(F_{score}\), and MCC of 90.56%, 87.22%, 86.87%, 93.21%, 85.88%, and, 80.14%, respectively. Figure 6 illustrates a set of training and validation accuracy/loss graphs generated by the proposed CSO-CADLCC model under distinct TR/TS data. From these figures, it can be understood that the validation accuracy is higher and the validation loss is lower, compared to the training accuracy/loss. Figure 6: Accuracy and loss analyses results of the CSO-CADLCC technique under distinct TR/TS data. (**a**) TA/VA data of 90:10, (**b**) TL/VL data of 90:10, (**c**) TA/VA data of 80:20, (**d**) TL/VL data of 80:20, (**e**) TA/VA data of 70:30, (**f**) TL/VL data of 70:30, (**g**) TA/VA data of 60:40, (**h**) TL/VL data of 60:40.