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August 26, 2025Science and Technology of Engineering Chemistry and Environmental ProtectionOpen Access

The Application of Machine Learning in the Diagnosis of Lung Cancer

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Authors

SLShengkun LiGuizhou University

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Implication

This review highlights the role of machine learning in diagnosing lung cancer, suggesting high accuracy via AI methods and addressing treatment challenges.

Key Points

  • High accuracy in lung cancer subtype classification was achieved using machine learning techniques, notably deep learning models.
  • Key methods included deep neural networks and convolutional neural networks applied to diverse medical data types like radiomics and histopathology.
  • Federated learning offers privacy-preserving solutions for collaborative training while facing challenges such as model interpretability and generalizability.
  • Future research must focus on interpretable AI frameworks to enable timely diagnosis and personalized treatment for lung cancer patients.

Cite This Study

Shengkun Li (2025) studied this question.

synapsesocial.com/papers/68af6210ad7bf08b1eae349chttps://doi.org/10.61173/78m59c45
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Lung Cancer in the New Era: Trends, Innovations, and Future Recommendations2026 · 1 citations
  2. 2Artificial Intelligence in Lung Cancer Management: Innovations, Challenges, and Clinical Translation2026
  3. 3Advancing Lung Cancer Classification through Machine Learning: A Comprehensive Comparative Analysis of Model Performance2024 · 1 citations
  4. 4Lung Cancer Prediction with Machine Learning, Deep Learning and Hybrid Techniques: A Survey2026 · 3 citations
  5. 5Application of machine learning in lung cancer prediction2024