DOI QR코드

DOI QR Code

Lung Cancer Classification and Detection Using Deep Learning Technique

  • K.Sudha Rani (EIE Dept. VNRVJIET Hyderabad Telangana) ;
  • A.Suma Latha (EIE Dept. VRSEC Vijayawada Andhra Pradesh) ;
  • S.Sunitha Ratnam (Dept.of Physics MRECW Hyderabad Telangana) ;
  • J.Bhavani (EEE Dept. VNRVJIET Hyderabad Telangana) ;
  • J.Srinivasa Rao (EEE Dept. VNRVJIET Hyderabad Telangana) ;
  • N.Kavitha Rao (ECE Dept. MVSREC Hyderabad Telangana)
  • Received : 2024.10.05
  • Published : 2024.10.30

Abstract

Lung cancer is a complex and frightening disease that typically results in death in both men and women. Therefore, it is more crucial to thoroughly and swiftly evaluate the malignant nodules. Recent years have seen the development of numerous strategies for diagnosing lung cancer, most of which use CT imaging. These techniques include supervisory and non-supervisory procedures. This study revealed that computed tomography scans are more suitable for obtaining reliable results. Lung cancer cannot be accurately predicted using unsupervised approaches. As a result, supervisory techniques are crucial in lung cancer prediction. Convolutional neural networks (CNNs) based on deep learning techniques has been used in this paper. Convolutional neural networks (CNN)-based deep learning procedures have produced results that are more precise than those produced by traditional machine learning procedures. A number of statistical measures, including accuracy, precision, and f1, have been computed.

Keywords

References

  1. Suren Makajua , P.W.C. Prasad, AbeerAlsadoona , A. K. Singhb , A. Elchouemic, "Lung Cancer Detection using CT Scan Images", 6th International Conference on Smart Computing and Communications, ICSCC 2017, Kurukshetra, India, 7-8 December 2017.
  2. Kazuhiro Suzuki, MD, PhD, Yujiro Otsuka, BSc, Yukihiro Nomura, RT, PhD, Kanako K. Kumamaru, MD, PhD, Ryohei Kuwatsuru, MD, PhD, Shigeki Aoki, MD, PhD, "Development and Validation of a Modified Three-Dimensional U-Net Deep-Learning Model for Automated Detection of Lung Nodules on Chest CT Images From the Lung Image Database Consortium and Japanese Datasets" Volume 29, pp.S11-S17, February 2022.
  3. Guobin Zhang, Shan Jiang , Zhiyong Yang, Li Gong, Xiaodong Ma, Zeyang Zhou, Chao Bao, Qi Liu "Automatic nodule detection for lung cancer in CT images: A review" Computers in Biology and Medicine , volume 103 , pp.287-300, 2018.
  4. P. Mohamed Shakeel , M.A. Burhan Uddin , Mohamad Ishak Desa "Lung cancer detection from CT image using improved profuse clustering and deep learning instantaneously trained neural networks" Volume 145, pp. 702-712, October 2019.
  5. R.Linder, T. Richards, and M. Wagner, "Microarray data classified by artificial neural Networks", Methods in Molecular Biology, Volume, pp.382, 345-72, 2007.
  6. Ying-Hwey Nai , Josh Schaefferkoetter , Daniel Fakhry-Darian a , Sophie O'Doherty , John J. Totman , Maurizio Conti , David W. Townsend , Arvind K. Sinha , Teng-Hwee Tan, Ivan Tham , Daniel C. Alexander ,Anthonin Reilha "Validation of low-dose lung cancer PET-CT protocol and PET image improvement using machine learning" Physica Medica, Volume 81, pp. 285-294, 2021.
  7. Sang Min Park, Min Kyung Lim, Soon Ae Shin,Young Ho Yun, "Impact of pre diagnosis smoking, Alcohol, Obesity and Insulin resistance on survival in Male cancer Patients: National Health Insurance corporation study" Journal of clinical Oncology, Volume 24, Issue 31,pp. 5017-24, November 2006.
  8. Yongqian Qiang, YouminGuo, Xue Li, Qiuping Wang, Hao Chen, Duwu Cuic, "The Diagnostic Rules of Peripheral Lung cancer Preliminary study based on Data Mining Technique", Journal of Nanjing Medical University, volume 21,issue 3,pp.190-195, 2007.
  9. Murat Karabhatak , M.CevdetInce, "Expert system for detection of breast cancer based on association rules and neural network" Expert systems with Applications, Volume 36, Issue 2, pp. 3465-3469,March 2009.
  10. ICMR Report 2006. Cancer Research in ICMR Achievements in Nineties, 2006.
  11. Osmar R.Zaiane, "Principles of Knowledge Discovery in Databases", Available:webdocs.cs.ualberta.ca/~zaiane/courses/cmput690//notes/Chapter1/ch1.pdf, 1999.
  12. Vijaya. Gajdhane Prof. Deshpande, "Detection of Lung Cancer Stages on CT scan Images by Using Various Image Processing Techniques ",IOSR Journal of Computer Engineering, Volume 16, Issue 5, pp. 28-35, September 2014.
  13. Harleen Kaur and Siri Krishan Wasan, "Empirical Study on Applications of Data Mining Techniques in Healthcare", Journal of Computer Science, volume 2, Issue 2, pp. 194-200, 2006.
  14. J.R. Quinlan, "Induction of decision trees. Machine learning", Volume 1, issue1, pp.81-106, 1986.
  15. L. Breiman, "Random forests", Machine learning, Volume 45, Issue 1,pp.5-32, 2001.
  16. R. Diaz-Uriarte, A. de Andre's, "Gene selection and classification of microarray data using random forest" BMC bioinformatics, volume 7, issue 1, 2006.
  17. R.S. Michal ski and K. Kaufman, "Learning patterns in noisy data: The AQ approach. Low dose Learning and its Applications", Springer Verilog, pages 22-38, 2001.
  18. S.Kalaivani, Pramit Chatterjee, Shikhar Juyal, Rishi Gupta , "Detection Using Digital Image Processing and Artificial Neural Networks", International Conference on Electronics, Communication and Aerospace Technology ICECA, Coimbatore, India, April 2017.