Deep learning has emerged as a powerful tool for medical image analysis and diagnosis, demonstrating high performance on tasks such as cancer detection. This literature review synthesizes current research on deep learning techniques applied to lung cancer screening and diagnosis. This review summarizes the state-of-the-art in deep learning for lung cancer detection, highlighting key advances, limitations, and future directions. We prioritized studies utilizing major public datasets, such as LIDC, LUNA16, and JSRT, to provide a comprehensive overview of the field. We focus on deep learning architectures, including 2D and 3D convolutional neural networks (CNNs), dual-path networks, Natural Language Processing (NLP) and vision transformers (ViT). Across studies, deep learning models consistently outperformed traditional machine learning techniques in terms of accuracy, sensitivity, and specificity for lung cancer detection in CT scans. This is attributed to the ability of deep learning models to automatically learn discriminative features from medical images and model complex spatial relationships. However, several challenges remain to be addressed before deep learning models can be widely deployed in clinical practice. These include model dependence on training data, generalization across datasets, integration of clinical metadata, and model interpretability. Overall, deep learning demonstrates great potential for lung cancer detection and precision medicine. However, more research is required to rigorously validate models and address risks. This review provides key insights for both computer scientists and clinicians, summarizing progress and future directions for deep learning in medical image analysis.

Accurate segmentation of Regions of Interest (ROI) in lung Computed Tomography (CT) is crucial for early lung cancer diagnosis and treatment planning. However, the variability in size, shape, and location of lung lesions, along with the complexity of 3D spatial relationships, poses significant challenges. In this work, we propose SALM (Segment Anything in Lung Model), a deep learning model for 2D and 3D ROI segmentation. SALM leverages Vision Transformers, proposing an adaptation of positional encoding functions to effectively capture spatial relationships in both 2D slices and 3D volumes using a single, unified model. Evaluation on the LUNA16 dataset demonstrated strong performance in both modalities. In 2D segmentation, SALM achieved a Dice score of 93% on 124,662 slices. For 3D segmentation using 174 3D images from the same dataset, SALM attained a Dice score of 81.88%. We also tested SALM on an external database (PleThora) on a subset of 255 pulmonary CT from diseased patients, where it achieved a Dice score of 78.82%. These results highlight SALM’s ability to accurately segment lung ROI in both 2D and 3D, demonstrating its potential to improve the accuracy and efficiency of computer-aided diagnosis for lung cancer.