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All issues Volume 194 (2024) SHS Web Conf., 194 (2024) 01006 References
Open Access
Issue
SHS Web Conf.
Volume 194, 2024
The 6th ETLTC International Conference on ICT Integration in Technical Education (ETLTC2024)
Article Number 01006
Number of page(s) 6
Section Intelligent Applications in Society
DOI https://doi.org/10.1051/shsconf/202419401006
Published online 26 June 2024
  1. M. A. Jaffar, A. Hussain, F. Jabeen, M. Nazir, and A. M. Mirza, “GA-SVM based lungs nodule detection and classification,” in Signal Processing, Image Processing and Pattern Recognition: International Conference, SIP 2009, Held as Part of the Future Generation Information Technology Conference, FGIT 2009, Jeju Island, Korea, December 10-12, 2009. Proceedings, 2009: Springer, pp. 133–140. [Google Scholar]
  2. Saba, T. (2019). Automated lung nodule detection and classification based on multiple classifiers voting. Microscopy research and technique, 82(9), 1601–1609. [CrossRef] [Google Scholar]
  3. S. T. Namin, H. A. Moghaddam, R. Jafari, M. EsmaeilZadeh, and M. Gity, “Automated detection and classification of pulmonary nodules in 3D thoracic CT images,” in 2010 IEEE international conference on systems, man and cybernetics, 2010: IEEE, pp. 3774-3779. vol. 34, no. 1, pp. 2395–2430, 2022 [Google Scholar]
  4. M. Tan, R. Deklerck, B. Jansen, M. Bister, and J. Cornelis, “A novel computer-aided lung nodule detection system for CT images,” Medical physics, vol. 38, no. 10, pp. 5630–5645, 2011 [CrossRef] [Google Scholar]
  5. S. Akram, M. Younus Javed, U. Qamar, A. Khanum, and A. Hassan, “Artificial neural network-based classification of lungs nodule using hybrid features from computerized tomographic images,” Applied Mathematics & Information Sciences, vol. 9, no. 1, pp. 183–195, 2015. [CrossRef] [Google Scholar]
  6. Shin, H. C., Kim, S. K., Lee, S. K., & Moon, M. G. (2016). Deep convolutional neural networks for medical image analysis. Annals of the New York Academy of Sciences, 1378(1), 206–216. [Google Scholar]
  7. National Cancer Institute. Cancer Statistics. 2023. [Online]. Accessed 5 Dec 2023. https://seer.cancer.gov/statfacts/ [Google Scholar]
  8. Siegel, R. L., Miller, K. D., & Jemal, A. (2023). Cancer statistics, 2023. CA: A Cancer Journal for Clinicians, 73(6), 363–414. [Google Scholar]
  9. Esteva, A., Kuprel, B., Novoa, R. A., Kohli, J., Thrun, J., & Wexler, J. (2014). Dermatologist-level classification of skin cancer with deep neural networks. Nature medicine, 20(3), 354–359. [Google Scholar]
  10. Zhang, C.; Sun, X.; Guo, X.; Zhang, X.; Yang, X.; Wu, Y.; Zhong, W. Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network. Oncologist 2019, 24, 1159–1165. [CrossRef] [Google Scholar]
  11. National Lung Screening Trial Research Team Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 2011;365:395–409. doi: 10.1056/NEJMoa1102873. [CrossRef] [Google Scholar]
  12. Chiang T.A., Chen P.H., Wu P.F., Wang T.N., Chang P.Y., Ko A.M., Huang M.S., Ko Y.C. Important prognostic factors for the long-term survival of lung cancer subjects in Taiwan. BMC Cancer. 2008; 8:324. doi: 10.1186/1471-2407-8-324. [CrossRef] [Google Scholar]
  13. Riquelme D., Akhloufi M.A. Deep Learning for Lung Cancer Nodules Detection and Classification in CT Scans. AI. 2020;1:28–67. doi: 10.3390/ai1010003. [CrossRef] [Google Scholar]
  14. Lakshmanaprabu S.K., Mohanty S.N., Shankar K., Arunkumar N., Ramirez G. Optimal deep learning model for classification of lung cancer on CT images. Future Gener. Comput. Syst. 2019;92:374–382. [CrossRef] [Google Scholar]
  15. Fernandes S.L., Gurupur V.P., Lin H., Martis R.J. A novel fusion approach for early lung cancer detection using computer aided diagnosis techniques. J. Med. Imaging Health Inform. 2017;7:1841–1850. doi: 10.1166/jmihi.2017.2280. [CrossRef] [Google Scholar]
  16. ICIRCA, 2021, Comparison of Conventional and Automated Machine Learning approaches for Breast Cancer Prediction [Google Scholar]
  17. 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2019, pp. 4, doi: 10.1109/ICECCT.2019.8869001.A Comparative Study of Lung Cancer Detection using Machine Learning Algorithms [Google Scholar]
  18. Kodavati, Trinaya; Rithani M.; Venkatraman K.; SyamDev R.S.; Detection and Classification of Arrhythmia Using Hybrid Deep Learning Model, 2023 [Google Scholar]
  19. Rithani M; Kumar, R. Prasanna; A review on big data based on deep neural network approaches, 2023. [Google Scholar]

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