Deep Learning Approaches for Lung Cancer Classification: A Comparative Study of Multiple Model Architectures

Early detection of lung cancer remains a critical challenge in medical imaging, where accurate classification between benign and malignant pulmonary nodules can significantly impact patient outcomes. This study presents a comprehensive comparative analysis of six distinct machine learning approaches for lung cancer classification using 3D computed tomography (CT) data. The methodologies evaluated include 2D convolutional neural networks (CNN), 3D CNN architectures, multi-slice processing techniques, and traditional machine learning algorithms including Random Forest and Logistic Regression. Our experimental framework utilized synthetic CT volumes designed to replicate characteristic patterns of benign and malignant lung lesions. The synthetic dataset comprised 120 volumetric samples with distinct spatial patterns representing clinical features such as nodule morphology, tissue density variations, and anatomical distribution. Results demonstrate that traditional machine learning approaches, particularly Random Forest classifiers, achieved superior performance with accuracy rates exceeding 85%, while deep learning models showed variable performance depending on architecture complexity and data preprocessing strategies. The findings suggest that careful consideration of model architecture selection and data representation is crucial for effective lung cancer classification systems.