A Survey on Early Lung Cancer Detection using a Deep Learning Algorithm

Abstract

Lung cancer remains a significant global factor in cancer-related deaths, making early diagnosis essential for enhancing patient outcomes. Conventional diagnostic methods, like low-dose computed tomography (LDCT), face significant false-positive rates and inconsistencies among evaluators. Recent breakthroughs in deep learning (DL) and transfer learning (TL) have transformed automated lung cancer detection, significantly boosting accuracy, scalability, and efficiency in the analysis of medical imaging data. This survey comprehensively examines the current landscape of DL and TL techniques applied to lung cancer diagnosis, focusing on three core tasks: nodule detection, malignancy classification, and tumor segmentation. We systematically review current architectures, including convolutional neural networks (CNNs), U-Net variants, 3D volumetric models, and emerging vision transformers (ViTs), alongside TL strategies that adapt pre-trained models (e.g., ResNet, DenseNet) to medical domains using datasets such as LIDC-IDRI, LUNA16, and NLST. Critical challenges are addressed, including data scarcity, class imbalance, model interpretability, computational costs, and limited generalizability across diverse populations and imaging protocols. The survey also highlights promising future directions, such as integrating multi-modal data (imaging, genomics), federated learning for privacy-preserving collaboration, explainable AI (XAI) frameworks for clinical trust and synthetic data generation to mitigate dataset biases. While DL and TL demonstrate transformative potential, their clinical translation requires overcoming technical bottlenecks, rigorous validation, and ethical considerations. This synthesis underscores the need for interdisciplinary collaboration to bridge the gap between algorithmic innovation and real-world deployment, finally advancing precision oncology and reducing global lung cancer mortality.