Systematic Literature Review of Deep Learning Techniques for Lung Cancer Segmentation

Abstract

Accurate lung nodule segmentation is essential for early lung cancer diagnosis and treatment planning. Although deep learning (DL) has significantly advanced automated segmentation, existing review studies often lack an integrated technical–clinical perspective and provide limited guidance for real‐world deployment. In this study, we conduct a systematic literature review following PRISMA 2020 guidelines, analysing 311 publications and synthesizing 113 high‐quality studies published between 2019 and 2026. This review makes three key contributions. First, we provide a structured comparison of major DL architectures, including convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs) and graph neural networks (GNNs), with respect to segmentation performance and computational efficiency. Second, we identify critical barriers to clinical adoption, such as data heterogeneity, annotation scarcity, high computational cost and limited model interpretability. Third, we analyse efficiency metrics (parameters, loss function and optimization) to categorize models suitable for point‐of‐care, resource‐constrained and mobile clinical environments. Based on these findings, we propose a roadmap for future research focusing on lightweight architectures, edge–cloud integration, federated learning and explainable AI (XAI). This study provides a clear and actionable framework to bridge the gap between DL‐based segmentation research and clinical deployment.