Lung disease classification is important for both diagnosis and treatment planning, particularly in biopsies and radiation therapy. Manual lung disease risk stratification is challenging because of (1) large lung data sizes, (2) inter- and intraobserver variability of the lung delineation, and (3) lack of feature amalgamation during the machine learning paradigm. This chapter presents a two-stage computer-aided diagnosis (CADx) cascaded system consisting of (1) a semiautomated lung delineation subsystem (LDS) for lung region extraction in computed tomographic (CT) slices followed by (2) a morphology-based lung tissue characterization, thereby addressing the above shortcomings. LDS uses primarily entropy-based region extraction, while machine learning (ML)–based lung characterization is based mainly on an amalgamation of directional transforms, such as Riesz and Gabor, along with texture-based features comprised of 100 grayscale features using the K-fold cross-validation protocol (K = 2, 3, 5, and 10). The lung database consisted of 96 patients: 15 normal and 81 diseased. We use five high-resolution CT levels representing different anatomy landmarks where disease is commonly seen. We demonstrate the amalgamated ML stratification accuracy of 99.53%, an increase of 2% against the conventional nonamalgamation ML system that uses only the Riesz-based feature embedded with feature selection based on feature strength. The robustness of the system was determined based on the reliability and stability that showed a reliability index of 0.99 and a deviation in risk stratification accuracies less than 5%. Our CADx system shows 10% better performance when compared against the mean of five other prominent studies available in the current literature covering over one decade.