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Deep Neural Network Based Classifier Model for Lung Cancer Diagnosis and Prediction System in Healthcare Informatics

  • D. JayarajEmail author
  • S. Sathiamoorthy
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 38)

Abstract

Lung cancer is a most important deadly disease which results to mortality of people because of the cells growth in unmanageable way. This problem leads to increased significance among physicians as well as academicians to develop efficient diagnosis models. Therefore, a novel method for automated identification of lung nodule becomes essential and it forms the motivation of this study. This paper presents a new deep learning classification model for lung cancer diagnosis. The presented model involves four main steps namely preprocessing, feature extraction, segmentation and classification. A particle swarm optimization (PSO) algorithm is sued for segmentation and deep neural network (DNN) is applied for classification. The presented PSO-DNN model is tested against a set of sample lung images and the results verified the goodness of the projected model on all the applied images.

Keywords

CT images Classifier Deep learning Lung cancer Segmentation 

References

  1. 1.
    World Health Organization: Description of the Global Burden of NCDs, Their Risk Factors and Determinants, Burden: Mortality, Morbidity and Risk Factors, pp. 9–32. World Health Organization, Switzerland (2011)Google Scholar
  2. 2.
    Siegel, R., Naishadham, D., Jemal, A.: Cancer statistics. CA Cancer J. Clin. 63, 11–30 (2013)CrossRefGoogle Scholar
  3. 3.
    Richard, D.: Lung cancer and other causes of death in relation to smoking. BMJ 2, 1071–1081 (1956)CrossRefGoogle Scholar
  4. 4.
    Ruth, L.K., Jeff, D., Declan, W.: Symptoms of lung cancer. Palliat. Med. 6, 309–315 (1992)CrossRefGoogle Scholar
  5. 5.
    Mark, S.W., Denise, M.Z., Edwin, B.F.: Depressive symptoms after lung cancer surgery: their relation to coping style and social support. Psychol. Oncol. 15, 684–693 (2005)Google Scholar
  6. 6.
    Jalal Deen, K., Ganesan, R., Merline, A.: Fuzzy-C-means clustering based segmentation and CNN-classification for accurate segmentation of lung nodules. Asian Pac. J. Cancer Prev. 18, 1869–1874 (2017)Google Scholar
  7. 7.
    Akhilesh Kumar, Y., Divya, T., Sonali, A.: Clustering of lung cancer data using foggy K-means. In: IEEE 2013 International Conference on Recent Trends in Information Technology (ICRTIT), pp. 13: 13–18 (2013)Google Scholar
  8. 8.
    Ying, W., Rui, L., Jin, Z.Y.: An algorithm for segmentation of lung ROI by mean-shift clustering combined with multiscale HESSIAN matrix dot filtering. J. Central South Univ. 19(12), 3500–3509 (2012)CrossRefGoogle Scholar
  9. 9.
    Qian, Y., Weng, G.: Lung nodule segmentation using EM algorithm. In: IEEE 2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics, pp. 14: 20–25 (2014). 24Google Scholar
  10. 10.
    Armato, I., Samuel McLennan, G., McNitt-Gray, F.R., Michael, Charles, Reeves, Anthony, P., Clarke, L.: Data From LIDC-IDRI. The Cancer Imaging Archive (2015). http://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAnnamalai UniversityChidambaramIndia
  2. 2.Tamil Virtual AcademyChennaiIndia

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