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Original Article
Lung Cancer Detection System
Aditya Raj1
Dr Hazique Aetesam2
1 2 Department of Computer Science & Engineering, Birla institute of technology mesra, Jharkhand, India.
Published Online: March-April 2026
Pages: 341-371
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260702042
References
1. Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021). Global Cancer Statistics 2020:
GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians,
71(3), 209-249.
2. American Cancer Society. (2024). Cancer Facts & Figures 2024. Atlanta: American Cancer Society.
3. de Groot, P. M., Wu, C. C., Carter, B. W., & Munden, R. F. (2018). The epidemiology of lung cancer. Translational Lung Cancer
Research, 7(3), 220-233.
4. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
5. Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3), 273-297.
6. Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society: Series B (Methodological),
20(2), 215-242.
7. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, E. (2011). Scikit-learn: Machine
Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
8. McKinney, W. (2010). Data structures for statistical computing in Python. Proceedings of the 9th Python in Science Conference, 445,
51-56.
9. Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., ... & Oliphant, T. E. (2020). Array
programming with NumPy. Nature, 585(7825), 357-362.
10. Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90-95.
11. Liao, F., Liang, M., Li, Z., Hu, X., & Song, S. (2019). A deep learning-based framework for lung cancer diagnosis on computed
tomography images. IEEE Access, 7, 89118-89127.
12. Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., ... & Kazerooni, E. A. (2011). The Lung
Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules
on CT scans. Medical Physics, 38(2), 915-931.
13. El-Baz, A., Beache, G. M., Gimel'farb, G., Suzuki, K., Okada, K., Elnakib, A., ... & Switala, A. (2013). Computer-aided diagnosis
systems for lung cancer: challenges and methodologies. International Journal of Biomedical Imaging, 2013, 942353.
14. Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., & Shen, D. (2015). Deep convolutional neural networks for multi-modalitysointense infant brain image segmentation. NeuroImage, 108, 214-224.
15. National Lung Screening Trial Research Team. (2011). Reduced lung-cancer mortality with low-dose computed tomographic screening.
New England Journal of Medicine, 365(5), 395-409.
16. World Health Organization. (2020). Global Health Observatory data repository: Cancer mortality and morbidity. Geneva: World
Health Organization.
17. Siegel, R. L., Miller, K. D., Wagle, N. S., & Jemal, A. (2023). Cancer statistics, 2023. CA: A Cancer Journal for Clinicians, 73(1), 233-
254.
18. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444
GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians,
71(3), 209-249.
2. American Cancer Society. (2024). Cancer Facts & Figures 2024. Atlanta: American Cancer Society.
3. de Groot, P. M., Wu, C. C., Carter, B. W., & Munden, R. F. (2018). The epidemiology of lung cancer. Translational Lung Cancer
Research, 7(3), 220-233.
4. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
5. Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3), 273-297.
6. Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society: Series B (Methodological),
20(2), 215-242.
7. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, E. (2011). Scikit-learn: Machine
Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
8. McKinney, W. (2010). Data structures for statistical computing in Python. Proceedings of the 9th Python in Science Conference, 445,
51-56.
9. Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., ... & Oliphant, T. E. (2020). Array
programming with NumPy. Nature, 585(7825), 357-362.
10. Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90-95.
11. Liao, F., Liang, M., Li, Z., Hu, X., & Song, S. (2019). A deep learning-based framework for lung cancer diagnosis on computed
tomography images. IEEE Access, 7, 89118-89127.
12. Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., ... & Kazerooni, E. A. (2011). The Lung
Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules
on CT scans. Medical Physics, 38(2), 915-931.
13. El-Baz, A., Beache, G. M., Gimel'farb, G., Suzuki, K., Okada, K., Elnakib, A., ... & Switala, A. (2013). Computer-aided diagnosis
systems for lung cancer: challenges and methodologies. International Journal of Biomedical Imaging, 2013, 942353.
14. Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., & Shen, D. (2015). Deep convolutional neural networks for multi-modalitysointense infant brain image segmentation. NeuroImage, 108, 214-224.
15. National Lung Screening Trial Research Team. (2011). Reduced lung-cancer mortality with low-dose computed tomographic screening.
New England Journal of Medicine, 365(5), 395-409.
16. World Health Organization. (2020). Global Health Observatory data repository: Cancer mortality and morbidity. Geneva: World
Health Organization.
17. Siegel, R. L., Miller, K. D., Wagle, N. S., & Jemal, A. (2023). Cancer statistics, 2023. CA: A Cancer Journal for Clinicians, 73(1), 233-
254.
18. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444
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