## 3 Results and Discussions
Figure 8: Lung cancer prediction performance based on gamma correction methods using DT course: (**a**) confusion matrix with gamma = 0.04, (**b**) confusion matrix with gamma = 0.5, (**c**) TPR (sensitivity) with gamma = 0.04, (**d**) TPR (sensitivity) with gamma = 0.5.  
significant results (\(p\)-value \(<\) 0.000000). In this study, we first applied image enhancement methods, such as gamma correction at different gamma values, contrast stretching at different thresholds, image adjustment, and histogram processing methods, and then computed the GLCM texture features. We obtained the improved detection results. The researchers in the existing studies did not utilize image enhancement methods and data augmentation techniques. Image enhancement methods on acquired data further improves the image quality, thereby improving the detection results. The proposed methods improved the classification results which can best be utilized by concerned health practitioners to further improve diagnostic capabilities. Figure 9: Cont. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Sensitivity** & **Specificity** & **PPV** & **NPV** & **Accuracy** & **FPR** & **AUC** \\ \hline Naive Bayes & 0.8686 & 0.9101 & 0.9386 & 0.8141 & 0.8847 & 0.08989 & 0.98 \\ \hline Decision Tree & 0.9911 & 0.986 & 0.9911 & 0.986 & 0.9891 & 0.01404 & 0.98 \\ \hline SVM Gaussian & 1 & 1 & 1 & 1 & 1 & 0 & 1 \\ \hline SVM RBF & 0.9982 & 1 & 1 & 0.9972 & 0.9989 & 0 & 1 \\ \hline SVM Poly. & 1 & 0.9972 & 0.9982 & 1 & 0.9989 & 0.002809 & 0.9999 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Lung cancer detection performance based on data augmentation with gamma correction (gamma = 0.5), and applying the ML methods. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Author** & **Features Used** & **Performance** \\ \hline \multirow{3}{*}{[PERSON] et al. [69]} & 1. Gradient & Sensitivity = 84\%, \\  & 2. Histogram & Specificity = 96\% \\  & 3. Spatial & Accuracy = 95\% \\ \hline \multirow{3}{*}{[PERSON] et al. [6]} & 1. GLCM & Sensitivity = 97\%, \\  & 2. Shape & Specificity = 94\% \\  & 3. Statistical & Accuracy = 95\% \\  & 4. Energy & & Sensitivity = 94\%, \\ \hline [PERSON] et al. [70] & 1. Statistical & Specificity = 90\% \\  & & AUC = 0.990 \\ \hline [PERSON] et al. [71] & Machine learning and deep learning methods to distinguish SCLC types & Accuracy =84.10\% \\ \hline [PERSON] et al. [72] & EfficientNet using transfer learning to distinguish NSCLC from SCLC & Accuracy = 90\% \\ \hline [PERSON] et al. [73] & Machine learning to classify subtypes of NSCLC & AUC = 0.972 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of findings with previous studies. Figure 9: Parallel prediction plots (**a**) using gamma correction at gamma = 0.04 on predictions, (**b**) gamma correction with gamma = 0.5 on predictions, (**c**) gamma correction with gamma = 0.04 data using 1_std_. For contrast stretching, we utilized the different threshold ranges, i.e., (0.1, 0.90; 0.02, 0.98; 0.05, 0.95). The interval ranges (0.02, 0.98) and (0.05, 0.95) yielded the highest improved detection performance, which indicates that these ranges are more appropriate to further enhance the image quality for this lung cancer dataset. The threshold range (0.02, 0.98) yielded further improved performance for the selected classifiers. Likewise, the image adjustment enhancement method also yielded higher detection performance for all the classifiers, similar to the contrast stretching range (0.02, 0.98). For gamma correction, we set different values of gamma, such as 0.04, 0.4, 0.5, 0.7, 0.9, and 4.0. The detection performance at lower and higher gamma values reduced the detection performance; however, the mid gamma values yielded a higher detection performance. The gamma value of 0.9 yielded the highest detection performance for all the classifiers, and the decision tree and naive Bayes algorithms yielded a higher detection performance than other contrast stretching and image adjustment methods.