Abstract: Highlights•Systematic review of key studies and trends in lung cancer detection.•Evaluation of ML and DL algorithms for lung cancer and their use cases.•Comparative analysis of 131 studies on detection and classification.•Guidance on designing efficient, interpretable lung cancer workflows.
External IDs:dblp:journals/eaai/CaiCZCWELZ25
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