REFERENCES
  1. Saadaldeen Rashid Ahmed Ahmed1, Israa Al_Barazanchi2, Ammar Mhana3 , Haider Rasheed and Abdulshaheed” Lung cancer classification using data mining and supervised learning algorithms on multi-dimensional data set” Periodicals of Engineering and Natural Sciences ISSN 2303-4521 Vol. 7, No. 2, June 2019, pp.438-447 2019.
  2. Jafar, ALzubi, Balasubramaniyan Bharathikannan, Sudeep Tanwar, Ramachandran Manikandan, Ashish Khanna, Chandrasekar Thaventhiran, “Boosted neural network ensemble classification for lung cancer disease diagnosis”, https://doi.org/10.1016/j.asoc.2019.04.031 , 1568-4946/© 2019.
  3. JAYADEEP PAT, “Gene Expression Analysis for Early Lung Cancer Prediction Using Machine Learning Techniques: An Eco-Genomics Approach”, http://www.ieee.org/publications_standards/publications/rights/index.html , IEEE 2169-3536 2018.
  4. James A. Bartholomai and Hermann B. Frieboes, “Lung Cancer Survival Prediction via Machine Learning Regression, Classification, and Statistical Techniques”, IEEE 978-1-5386-7568-7/18/$31.00 ©2018.
  5. Mohamad Rabbani, Jonathan Kanevsky, Kamran Kafi, Florent Chandelier and Francis J. Giles, “Role of artificial intelligence in the care of patients with nonsmall cell lung cancer”, https://doi.org/10.1111/eci.12901 , © 2018.
  6. Gur Amrit Pal Singh and P. K. Gupta, “Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans”, https://doi.org/10.1007/s00521-018-3518-x , The Natural Computing Applications Forum 27 April 2018.
  7. Muhammad Imran Faisal, Saba Bashir, Zain Sikandar Khan, Farhan Hassan Khan,” An Evaluation of Machine Learning Classifiers and Ensembles for Early Stage Prediction of Lung Cancer” 2020.
  8. Darcie A. P. Delzell, Sara Magnuson, Tabitha Peter, Michelle Smith and Brian J. Smith, “Machine Learning and Feature Selection Methods for Disease Classification With Application to Lung Cancer Screening Image Data”, https://doi.org/10.3389/fonc.2019.01393 , December 2019.
  9. Lakshmanaprabu S.K, Sachi Nandan mohanty, Shankar K , Arunkumar N and Gustavo Ramirez, ” Optimal Deep Learning Model for Classification of Lung Cancer on CT Images”, https://doi.org/10.1016/j.future.2018.10.009 , 4 October 2018.
  10. Jay Kumar Raghavan Nair, Umar Abid Saeed, Connor C. McDougall, Ali Sabri, Mmed, Bojan Kovacina, B. V. S. Raidu, Riaz Ahmed Khokhar, Stephan, Vera Hirsh, Chankowsky Jeffrey, Leon C. Van Kempen, and Jana Taylor, “Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer”, https://doi.org/10.1177/0846537119899526 , The Author(s) 2020.
  11. Diego Ardila , Atilla P. Kiraly, Sujeeth Bharadwaj, Bokyung Choi, Joshua J. Reicher , Lily Peng, Daniel Tse , Mozziyar Etemadi, Wenxing Ye, Greg Corrado, David P. Naidich and Shravya Shetty, “End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography”, https://doi.org/10.1038/s41591-019-0447-x , http://www.nature.com/naturemedicine JUNE 2019.
  12. Ibrahim M. Nasser and Samy S. Abu-Naser, “Lung Cancer Detection Using Artificial Neural Network”, International Journal of Engineering and Information Systems (IJEAIS), https://ssrn.com/abstract=3369062 , March – 2019.
  13. Yiwen Xu, Ahmed Hosny, Roman Zeleznik, Chintan Parmar, Thibaud Coroller, Idalid Franco, Raymond H. Mak, and Hugo J.W.L. Aerts, “Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging”, 10.1158/1078-0432.CCR-18-2495 , American Association for Cancer Research, http://clincancerres.aacrjournals.org/ , April 23 2019.
  14. Nasrullah Nasrullah, Jun Sang, Mohammad S. Alam, Muhammad Mateen, Bin Cai and Haibo Hu, “Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies”,http://www.mdpi.com/journal/sensors , 26 August 2019.
  15. Kun-Hsing Yu, Tsung-Lu Michael Lee, Ming-Hsuan Yen, S C Kou, Bruce Rosen, Jung-Hsien Chiang and Isaac S Kohane, “Reproducible Machine Learning Methods for Lung Cancer Detection Using Computed Tomography Images: Algorithm Development and Validation”,http://dx.doi.org/10.2196/16709 , JOURNAL OF MEDICAL INTERNET RESEARCH, J Med Internet Res 2020.
  16. Ning Langa, Yang Zhangb, Enlong Zhanga, Jiahui Zhanga, Daniel Chowb, Peter Changb, Hon J. Yub, Huishu Yuana and Min-Ying Su, “Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI”, https://www.elsevier.com/locate/mri ,https://doi.org/10.1016/j.mri.2019.02.013 , d 28 February2019.
  17. Janee Alam, Sabrina Alam and Alamgir Hossan, “Multi-Stage Lung Cancer Detection and Prediction Using Multi-class SVM Classifier”, 2019.
  18. Hann-Hsiang Chaol, Gilmer Valdes, Jose M. Luna, Marina Heskel, Abigail T. Berman, Timothy D. Solberg and Charles B. Simone, ” Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non‐small‐cell lung cancer treated with stereotactic body radiation therapy”,http://www.wileyonlinelibrary.com/journal/JACMP , Accepted: 13 June 2018.
  19. Ahmed Hosny, Chintan Parmar, Thibaud P. Coroller, Patrick Grossmann, Roman Zeleznik, Avnish Kumar, Johan Bussink, Robert J. Gillies, Raymond H. Mak and Hugo J. W. L. Aerts, “Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study”, PLOS Medicinehttps://doi.org/10.1371/journal.pmed.1002711, November 30, 2018.
  20. Ruchita Tekade and Pro. Dr. k. Rajeswari, “Lung Cancer detection and Classification using Deep learning ”, 2018 Fourth International Conference on computing communication and Automation, IEEE 2018.