Predictive radiomics based ensemble machine learning approach in CT lung nodule diagnosis

Lung Cancer remains the most common and deadly type of cancer. Early detection of lung lesions and nodules is crucial in the fight against lung cancer. The purpose of this study was to investigate radiomics based on geometrical, texture, and Daubechies WPT texture features for quantitative CT image analysis. The LIDC database was used in this study. Geometrical features, texture features based on three statistical methodologies (GLCM, GLDM GLRLM) and Daubechies WPT texture features are retrieved from the nodules. Using the ensemble EFS, BOCET and BACET, pertinent features were identified. Lastly, various cutting-edge ML classifiers were used to classify LC as malignant or benign. The out-turn shows that, using BACET EFS, Ensemble Subspace KNN gives best AUROC (93.4%), accuracy (88.3%) and F1-score (85.2%). FGSVM yields the best sensitivity of 97.1%. RUSBOCET gives best precision and best specificity of 93.4% and 83.1% respectively. Therefore, the methodology can be applied with efficacy to the CT based PN classification. Thus, the result can assist medical professionals in making better decisions and interventions.