## 3 Results and Discussions
Figure 8 reflects the lung cancer prediction performance using the decision tree model by extracting GLCM-based texture features and applying image enhancement gamma correction methods with gamma = 0.04 and 0.5 using 10-fold cross-validation techniques. The corresponding prediction plots with _mean \(\pm\) standard deviation_ are reflected in Figure 9. Using gamma correction at gamma = 0.04, the NSCLC TP predicted are 318, and 38 were FP. Likewise, for SCLC, 552 were predicted as TN, and 11 were predicted as FN. Using gamma correction at gamma = 0.5, NSCLC yielded 349 as TP, and 7 as FP, whereas SCLC yielded 559 as TN, and 4 as FN. In Figure 8, the blue color represents the NSCLC, and the red color represents the SCLC. The solid line represents correct predictions, and the bold line with crosses represents incorrect predictions. As the confusion matrix indicates, using gamma = 0.04, there were 38 misclassification examples represented as FP, i.e., NSCLC misclassified to SCLC, and 11 examples were misclassified as FN, i.e., SCLC misclassified to NSCLC. In the case of gamma = 0.5, for NSCLC, only seven examples were misclassified to SCLC, and for SCLC, only four examples were misclassified to NSCLC, as reflected in Figure 9a,b. Figure 9c presents the predictions of NSCLC and SCLC plotted within the 1\(std\). The misclassification examples are highlighted with a bold black line and cross symbols at different extracted features. Figure 6: Lung cancer detection based on gamma correction method at different selected gamma values: (**a**) 0.04, (**b**) 0.3, (**c**) 0.5, (**d**) 0.7, (**e**) 0.9, (**f**) 4.0. Figure 7: GLCM-based texture features by applying an image enhancement method to lung cancer detection, and applying ML techniques using 1 to 10 cross-fold validation: (**a**) SVM polynomial, (**b**) SVM RBF, (**c**) SVM Gaussian, (**d**) decision tree, (**e**) naïve Bayes. Table 1 reflects the results based on data augmentation methods to avoid overfitting. We utilized the augmentation methods using 'RandRotation'(=(\(-20,20\)), 'RandXReflection' = 1, 'RandXReflection' = 1, 'RandXfReflection' = (\(-3\), \(3\)), and 'RandYTranslation' = (\(-3\), \(3\)) on GLCM features extracted from lung cancer SCLC and NSCLC, and then applied the image enhancement method gamma correction with gamma = 0.5. Using data augmentation, we obtained mostly similar results as those obtained without data augmentation. The highest accuracy was yielded using SVM Gaussian, with 100% accuracy and 1.0 of AUC; followed by SVM RBF, with accuracy (99.89%), AUC (1.0); SVM polynomial, with accuracy (99.89%), AUC (0.9999); decision tree, with accuracy (98.91%), AUC (0.98); and naive Bayes, with accuracy (98.91%) and AUC (0.98). Table 2 reflects the lung cancer detection results using different feature extraction and classification methods. In the past, researchers utilized different automated approaches to detect lung cancer. [PERSON] et al. [69] applied different features of extraction methods, such as gradient, histogram, and spatial methods, and obtained an overall accuracy of 95%. [PERSON] et al. [6] also extracted different features, such as GLCM, shape-based features, and statistical and energy features, and achieved an accuracy of 95%; however, a sensitivity of 97% was obtained, and a specificity of 96%. [PERSON] et al. [70] computed the statistical features and obtained a sensitivity of 94%, specificity of 90%, and AUC of 0.990. [PERSON] et al. [71] used machine learning techniques to distinguish the SCLC types, and achieved an accuracy of 84.10%. [PERSON] et al. [72] applied EfficientNet to deep learning, and obtained a highest accuracy of 90%. [PERSON] et al. [13] computed different entropic-based features and computed the nonlinear dynamics to distinguish the SCLC from NSCLC with the highest accuracy.