Early Lung Cancer Detection using Artificial Intelligence and Deep Learning Approach: A Review

Early detection of lung cancer through advanced imaging techniques has significantly improved patient prognosis and survival rates. This review paper comprehensively examines the state-of-the-art artificial intelligence and deep learning methodologies employed for early lung cancer detection. We synthesize findings from thirty peer-reviewed publications, covering convolutional neural networks (CNNs), recurrent neural networks (RNNs), transfer learning approaches, and ensemble methods. The paper analyzes diverse datasets including CT scans, chest X-rays, and multimodal imaging data, evaluating performance metrics such as sensitivity, specificity, accuracy, and AUC-ROC scores. Our systematic review reveals that hybrid deep learning architectures combined with transfer learning achieve detection accuracies exceeding 96% on benchmark datasets. However, challenges remain in addressing class imbalance, limited annotated datasets, interpretability concerns, and clinical validation requirements. This paper identifies research gaps and proposes future directions for developing clinically viable AI systems for lung cancer screening. The integration of explainable AI techniques, larger multi-institutional datasets, and federated learning approaches are highlighted as critical areas for advancement. Our findings indicate that AI-assisted diagnostic systems show considerable promise in augmenting radiologist capabilities and enabling timely intervention.