## 3 Results and Discussions  
This study was specifically conducted to improve lung cancer prediction performance by optimizing feature extraction and machine learning methods. We first extracted the 22 gray-level co-occurrences (GLCM) features, and applied vigorous ML algorithms to classify the NSCLC from SCLC. We then used an image enhancement method, i.e., image adjustment, before extracting the GLCM feature, and then applied machine learning algorithms. The results reveal that the prediction performance based on image enhancement methods was improved with all the classification algorithms. Figure 3a reflects the texture features extracted from NSCLC and SCLC MRI images. We utilized the supervised machine learning algorithms. The SVM Gaussian and RBF provided the highest performance, with sensitivity (99.82%), specificity and PPV (100%), and accuracy (99.89%), followed by SVM polynomial, with sensitivity and NPV (100%), specificity (99.72%), and accuracy (99.89%). The decision tree yielded an accuracy of 99.35%, whereas the naive Bayes yielded an accuracy of 88.57%. Figure 3b reflects the lung cancer performance detection by first applying the image adjustment method, and then computing the texture features from NSCLC and SCLC MRI images. The highest performance was obtained using SVM Gaussian and RBF, with 100% performance metrics; followed by SVM polynomial, with accuracy (99.89%) and AUC (0.9999); decision tree, with accuracy (98.37%) and AUC (0.9649); naive Bayes, with accuracy (84.87%) and AUC (0.9649). Figure 3c reflects the lung cancer performance detection by first applying the gray-level thresholding method, and then computing the texture features from NSCLC and SCLC MRI images. The highest performance was obtained using SVM polynomial, yielding an accuracy (99.46%) and AUC (0.9971); followed by SVM polynomial, with accuracy (97.93%) and AUC (0.9977); SVM Gaussian, with accuracy (99.71%) and AUC (0.9731); decision tree, with accuracy (96.52%) and AUC (0.9731); and naive Bayes, with accuracy (0.9652) and AUC (0.9731). Figure 3d reflects the lung cancer performance detection by first applying the histogram equalization method, and then computing the texture features from NSCLC and SCLC MRI images. SVM polynomial provided accuracy (97.82%) and AUC (0.9971); followed by SVM RBF, with accuracy (96.84%) and AUC (0.9912); SVM Gaussian, with accuracy (96.30%) and  
Figure 2: K-fold cross-validation. Figure 4 reflects the AUC to discriminate the NSCLC from SCLC by computing texture features and applying the image adjustment method and (b) the gamma correction method with a gamma value of 0.04. We then applied the supervised machine learning algorithms. The highest separation using image adjustment (Figure 4a) was yielded using SVM Gaussian and RBF, with AUC (1.00); followed by SVM polynomial, with AUC (0.999); and decision tree and naive Bayes, with AUC (0.9649). The highest separation by extracting gamma correction with a gamma value of 0.04 (Figure 4b) was obtained using SVM RBF, with AUC (0.9956); followed by SVM polynomial, with AUC (0.9930); SVM Gaussian, with AUC (0.9941); and decision tree and naive Bayes, with AUC (0.9821). Figure 5 indicates the lung cancer detection based on the image enhancement method, e.g., contrast stretching at different selected thresholds. Figure 5a utilized a threshold of (0.05, 0.95). The highest detection performance was yielded using SVM Gaussian and SVM RBF, with 100% performance; followed by SVM polynomial, with accuracy (99.67%), AUC (0.9916); decision tree, with accuracy (98.37%), AUC (98.37%), AUC (0.9518); naive Bayes, with accuracy (81.37%), AUC (0.9518). Figure 5b utilized a threshold of (0.01, 0.90). The SVM Gaussian and SVM polynomial provided the highest accuracy (99.89%), with AUC (0.9999); followed by SVM RBF, with accuracy (99.56%), AUC (0.9999); SVM Gaussian, with accuracy (99.46%), AUC (0.9999); decision tree, with accuracy (99.24%), AUC (0.9613); and naive Bayes, with accuracy (80.63%), AUC (0.9613). Figure 5c utilized a threshold of (0.02, 0.98).