Authors : I. Saleth Mary; Dr. A. Shanthasheela

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/2s42xyt8

Scribd : https://tinyurl.com/2tmrdpru

DOI : https://doi.org/10.38124/ijisrt/25apr1725

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Abstract : Lung cancer is an unregulated development of cells that begins in the lung and spreads to other parts of the body, posing a significant risk to human life. Radiological imaging, such as computed tomography (CT) scans and X-rays, is the primary tool for diagnosing lung cancer. However, a person's ability to interpret a large number of CT images might vary greatly, especially when the scans show many gray level fluctuations. The purpose of this study is to use Python-based machine learning and image processing approaches to detect lung cancer. Using the National Center for Cancer Diseases lung cancer dataset, this paper analyzes lung scans to determine if they are malignant or non-cancerous. Based on the study's top-performing solution, the code first preprocesses the images before applying segmentation and feature extraction techniques. The suggested approach makes a cancer prediction based on retrieved properties that were obtained through morphological processing.

Keywords : Lung Cancer, Segmentation, Malignant, Morphological Operations, CNN.

References :

1. Liu, X., Song, L., Liu, S., & Zhang, Y. (2021). A review of deep-learning-based medical image segmentation methods. Sustainability, 13(3), 1224.
2. Soni, A., & Singh, A. P. (2020, February). Automatic Pulmonary Cancer Detection using Prewitt & Morphological Dilation. In 2nd International Conference on Data, Engineering and Applications (IDEA) (pp. 1-6). IEEE.
3. Ahmed, T., Parvin, M. S., Haque, M. R., & Uddin, M. S. (2020). Lung cancer detection using CT image based on 3D convolutional neural network. Journal of Computer and Communications, 8(03), 35.
4. Sangeetha, M., & Mythili, S. (2021). Detection and Characterization of Lung Tumor by Using Convolution Neural Network. In Journal of Physics: Conference Series (Vol. 1717, No. 1, p. 012004). IOP Publishing.
5. Thakur, S. K., Singh, D. P., & Choudhary, J. (2020). Lung cancer identification: a review on detection and classification. Cancer and Metastasis Reviews, 39(3), 989-998.
6. Muthee, M. G., Makau, M., & Amos, O. (2022). A review of techniques for morphological analysis in natural language processing. African Journal of Science, Technology and Social Sciences, 1(2), 93-103.
7. Chen, S., Zhao, M., Wu, G., Yao, C., & Zhang, J. (2012). Recent advances in morphological cell image analysis. Computational and mathematical methods in medicine, 2012(1), 101536.
8. Ding, C., Aye, H. T. Z., Pa, W. P., Nwet, K. T., Soe, K. M., Utiyama, M., & Sumita, E. (2019). Towards Burmese (Myanmar) morphological analysis: Syllable-based tokenization and part-of-speech tagging. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 19(1), 1-34.
9. Wang, W., Fam, R., Bao, F., Lepage, Y., & Gao, G. (2019, July). Neural morphological segmentation model for Mongolian. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE.
10. Ansari, E., Žabokrtský, Z., Mahmoudi, M., Haghdoost, H., & Vidra, J. (2019, September). Supervised morphological segmentation using rich annotated lexicon. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019) (pp. 52-61).  
11. Sang, J., Alam, M. S., & Xiang, H. (2019, May). Automated detection and classification for early stage lung cancer on CT images using deep learning. In Pattern recognition and tracking XXX (Vol. 10995, pp. 200-207). SPIE.
12. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 (pp. 234-241). Springer International Publishing.
13. Soni, A., & Singh, A. P. (2020, February). Automatic Pulmonary Cancer Detection using Prewitt & Morphological Dilation. In 2nd International Conference on Data, Engineering and Applications (IDEA) (pp. 1-6). IEEE.
14. Kuruvilla, J., & Gunavathi, K. (2013). Detection of lung cancer using morphological operations. International Journal of Scientific & Engineering Research, 4(8), 1636-1639.
15. Hussain, L., Rathore, S., Abbasi, A. A., & Saeed, S. (2019, March). Automated lung cancer detection based on multimodal features extracting strategy using machine learning techniques. In Medical imaging 2019: physics of medical imaging (Vol. 10948, pp. 919-925). SPIE.
16. Soni, A., Rai, A., & Shivhare, E. (2019). Atomatic Lung Cancer Detection using Sobel & Morphological Operations. International Journal of Engineering and Advanced Technology (IJEAT).
17. Vas, M., & Dessai, A. (2017, August). Lung cancer detection system using lung CT image processing. In 2017 international conference on computing, communication, control and automation (ICCUBEA) (pp. 1-5). IEEE.
18. Kalaivani, S., Chatterjee, P., Juyal, S., & Gupta, R. (2017, April). Lung cancer detection using digital image processing and artificial neural networks. In 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA) (Vol. 2, pp. 100-103). IEEE.
19. Li, R., Xiao, C., Huang, Y., Hassan, H., & Huang, B. (2022). Deep learning applications in computed tomography images for pulmonary nodule detection and diagnosis: A review. Diagnostics, 12(2), 298.
20. Luo, X., Zang, X., Yang, L., Huang, J., Liang, F., Rodriguez-Canales, J & Xiao, G. (2017). Comprehensive computational pathological image analysis predicts lung cancer prognosis. Journal of Thoracic Oncology, 12(3), 501-509.
21. Biradar, V. G., Pareek, P. K., & Nagarathna, P. (2022, October). Lung Cancer detection and classification using 2D convolutional neural network. In 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon) (pp. 1-5). IEEE.
22. Karim, D. Z., & Bushra, T. A. (2021, December). Detecting lung cancer from histopathological images using convolution neural network. In TENCON 2021-2021 IEEE Region 10 Conference (TENCON) (pp. 626-631). IEEE.
23. Abdul, W. (2020, December). An automatic lung cancer detection and classification (ALCDC) system using convolutional neural network. In 2020 13th International Conference on Developments in eSystems Engineering (DeSE) (pp. 443-446). IEEE.
24. Sasikala, S., Bharathi, M., & Sowmiya, B. R. (2018). Lung cancer detection and classification using deep CNN. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(2S).
25. Perez, G., & Arbelaez, P. (2020). Automated lung cancer diagnosis using three-dimensional convolutional neural networks. Medical & Biological Engineering & Computing, 58, 1803-1815.