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March 3, 2026Open Access

Lung Cancer Prediction with Machine Learning, Deep Learning and Hybrid Techniques: A Survey

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Authors

AZAbdullah Bin ZahidFNFakhar Un NisaAMAhmad Kamran Malik

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Overview

This survey explores diverse AI-based methods for early lung cancer detection, highlighting their importance in improving patient outcomes.

Key Points

  • This research aims to enhance lung cancer diagnosis through various learning methodologies and innovative diagnostic solutions.
  • Reviewed studies from the last 10 years on lung cancer prediction methods.
  • Analyzed the impact of machine learning and deep learning techniques.
  • Evaluated the role of automated diagnostic systems in reducing human error.
  • AI methodologies show potential for early detection of lung cancer.
  • Emphasized the importance of comprehensive screening programs.
  • Highlighted a significant reduction in late-stage diagnoses through improved prediction methods.

Cite This Study

Zahid et al. (2026) studied this question.

synapsesocial.com/papers/69a67f12f353c071a6f0aecdhttps://doi.org/10.3390/labmed3010007
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Also Consider

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

  1. 1A Journeying on Machine Learning and Deep Learning Strategies for Lung Carcinoma Forecasting2026
  2. 2Lung Cancer in the New Era: Trends, Innovations, and Future Recommendations2026
  3. 3Application of machine learning in lung cancer prediction2024
  4. 4Predictive Modeling for Lung Cancer Detection: Unveiling Insights through Machine Learning Techniques2024
  5. 5The Application of Machine Learning in the Diagnosis of Lung Cancer2025