Harnessing Deep Learning for Lung Cancer Detection and Prevention: A Comprehensive Survey

Lung cancer remains one of the deadliest cancers worldwide, where early identification and prompt action are essential to enhancing patient outcomes. This survey investigates how deep learning is changing the landscape of lung cancer detection and prevention, with attention to recent breakthroughs in medical imaging analysis. Deep learning models have had some success in accurately classifying lung nodules and other CT abnormalities using convolutional neural networks (CNNs), as well as advanced architecture such as ResNet and U-Net. This review systematically covers a selection of state-of-the-art methods such as transfer learning or data augmentation techniques that deal with problems such as limited annotated datasets and model interpretability. We also illustrate strategies for embedding those emerging tools into the clinical practice workflow to improve early diagnosis and risk stratification for preventive care. This survey highlights the dynamic evolution of lung cancer research and prevention through deep learning approach and provides significant insights and a definitive roadmap for future work toward the application of this technology to combat the disease.