Advances in Deep Learning Approaches for Lung Cancer Diagnosis

Lung cancer is still the number one cause of cancer related deaths globally and early detection is key to improving survival Low-dose CT (LDCT) screening has been demonstrated to reduce mortality however is limited by both high false-positive rates and heavy radiologist workload. Deep learning (DL) has become a paradigm-shifting technology in the automation and improvement of lung cancer diagnosis. This article reviews developments of DL in this field from the early CNNs for the detection and classification of nodules to sophisticated architectures such as U-Net and Transformers for accurate segmentation. We highlight the trend towards multimodal analysis that combine imaging and clinical data, including patient history, smoking status, and prior scans leading to better characterization models. We also emphasize the increasing significance of Explainable AI (XAI) for understanding model decisions and enabling clinical trust. Challenges such as data paucity, model generalization, and clinical practice integration remain unresolved despite encouraging findings. Desirable future directions include self-supervised learning, federated learning for privacy-preserving collaboration and robust prospective validation. The intersection of multimodal DL and XAI also offers the opportunity to create strong DSSs, allowing for earlier and more accurate LC diagnoses, leading to enhanced patient survival.