## 3 Results and Discussions
The maximum detection performance was yielded using SVM Gaussian and SVM RBF,  
Figure 3: Detecting lung cancer based on different image enhancement methods: (**a**) texture features only, (**b**) image adjustment, (**c**) gray-level thresholding, (**d**) histogram equalization. with accuracy (100%), AUC (1.00); followed by SVM polynomial, with accuracy (99.89%), AUC (0.9994); decision tree, with accuracy (98.48%), AUC (0.9465); and naive Bayes, with accuracy (84.11%), AUC (0.9465). Figure 4: Area under the receiver operating characteristic (AUC) curve: (**a**) texture with image adjustment, (**b**) gamma correction at gamma value of 0.04 to distinguish the NSCLC from SCLC. Figure 5: Lung cancer detection based on contrast stretching at different selected threshold ranges: (**a**) (0.05–0.95), (**b**) (0.01–0.90), (**c**) (0.02–0.98). Figure 6 indicates lung cancer detection based on the gamma correction method at different selected gamma values. Figure 6a utilized a gamma value of 0.04. The DT produced the highest detection performance, with accuracy (98.15%), AUC (0.9821); followed by naive Bayes, with accuracy (92.49%), AUC (0.9821); SVM polynomial, with accuracy (70.84), AUC (0.993); SVM RBF, with accuracy (69.21%), AUC (0.7472); and SVM Gaussian, with accuracy (0.6910), AUC (0.9941). Figure 6b utilized a gamma value of 0.3. The highest detection performance was yielded using SVM polynomial, with accuracy (98.69%), AUC (1.00); followed by decision tree, with accuracy (98.59%), AUC (0.9856); SVM Gaussian, with accuracy (96.84%), AUC (0.9989); SVM RBF, with accuracy (96.52), AUC (0.9989); SVM RBF, with accuracy (69.21%), AUC (0.7472); SVM Gaussian, with accuracy (0.6910), AUC (0.9941); and naive Bayes, with accuracy (94.56%), AUC (0.9856). Figure 6c utilized a gamma value of 0.5. The SVM RBF yielded a higher performance, with accuracy (100%), AUC (1.00); followed by SVM Gaussian and polynomial, with accuracy (99.89%), AUC (0.9986); decision tree, with accuracy (99.134%), AUC (0.9469); and naive Bayes, with accuracy (82.59%), AUC (0.9469). Figure 6d utilized a gamma value of 0.7. The SVM Gaussian produced the highest detection performance, with accuracy (100%), AUC (1.00); followed by SVM RBF and polynomial, with accuracy (99.89%), AUC (0.9987); decision tree, with accuracy (98.91%), AUC (0.9664); and naive Bayes, with accuracy (85.53%), AUC (0.9664). Figure 6e utilized a gamma value of 0.7. The SVM Gaussian and RBF provided accuracy (100%), AUC (1.00); followed by SVM polynomial, with accuracy (99.89%), AUC (0.9991); decision tree, with accuracy (99.24%), AUC (0.9795); and naive Bayes, with accuracy (88.14%), AUC (0.9795). Figure 6f utilized a gamma value of 4.0. The highest detection performance was yielded using SVM polynomial, with accuracy (%), AUC (1.00); followed by SVM polynomial, with accuracy (99.89%), AUC (0.9987); decision tree, with accuracy (98.91%), AUC (0.9664); Figure 6e utilized a gamma value of 0.7. The SVM Gaussian and RBF provided accuracy (100%), AUC (1.00); followed by SVM polynomial, with accuracy (99.89%), AUC (0.9991); decision tree, with accuracy (99.24%), AUC (0.9795); and naive Bayes, with accuracy (88.14%), AUC (0.9795). Figure 6f utilized a gamma value of 4.0. The highest detection performance was yielded using SVM polynomial, with accuracy (%), AUC (1.00); followed by SVM polynomial, with accuracy (99.89%), AUC (0.9991); decision tree, with accuracy (99.24%), AUC (0.9795); and naive Bayes, with accuracy (88.14%), AUC (0.9795). Figure 7 reflects the AUC separation at different folds from 1 to 10 for each of the classification algorithms. The maximum AUC was covered at all the folds for SVM Gaussian, RBF, polynomial, and decision tree, whereas the naive Bayes yielded a slight lower AUC for folds 1 to 10 using gamma correction at a gamma value of 0.04.