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Enhancing Lung Cancer Detection: A Comparative Analysis of CNN and RNN Models on X-Ray Image Data

Author

Listed:
  • Bodicherla Siva Sankar

    (Research Scholar, Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University, India)

  • D Natarajasivan

    (Assistant Professor, Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University, India)

  • M Purushotham Reddy

    (Professor and Head, Department of Information Technology, Institute of Aeronautical Engineering, India)

Abstract

Lung cancer originates from abnormal growth in lung cells, characterized by uncontrolled cell division within lung tissues. Early detection of lung cancer is crucial for improving patient outcomes and survival rates. The cited papers highlight several limitations, such as small sample sizes, reliance on previous studies, the need for future validation, resource-intensive methods, potential biases, lack of longitudinal data, necessity for further experimentation, limited clinical practice integration, dependence on imaging quality, and insufficient data for robust model training and evaluation.

Suggested Citation

  • Bodicherla Siva Sankar & D Natarajasivan & M Purushotham Reddy, 2024. "Enhancing Lung Cancer Detection: A Comparative Analysis of CNN and RNN Models on X-Ray Image Data," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 58(2), pages 50150-50179, August.
    • Handle: RePEc:abf:journl:v:58:y:2024:i:2:p:50150-50179
    DOI: 10.26717/BJSTR.2024.58.009131
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