Skip to main content

Analysis of the Classification Methods of Cancer Types by Computer Tomography Images

Part of the Communications in Computer and Information Science book series (CCIS,volume 674)

Abstract

The present work is aimed at improving the efficiency of selection of traits in order to increase the information value of the checked pulmonary node, as well as the comparative evaluation of machine learning algorithms for classification in CT images.

Keywords

  • Machine learning
  • Computer tomography
  • Classification methods

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-49700-6_52
  • Chapter length: 6 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   89.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-49700-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   119.99
Price excludes VAT (USA)
Fig. 1.

References

  1. Armato III, S.G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Zhao, B., Aberle, D.R., Henschke, C.I., Hoffman, E.A.: The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38, 915–931 (2011)

    CrossRef  Google Scholar 

  2. Bankmann, N.: Handbook of Medical Imaging. Academic, New York (2000)

    Google Scholar 

  3. Kononenk, I.: Machine learning for medical diagnosis. History, state of the art and perspective. Artif. Intell. Med. 23, 89–109 (2001)

    CrossRef  Google Scholar 

  4. Dettori, L., Semler, L.: A comparison of wavelet, ridgelet, and curvelet based texture classification algorithm in computed tomography. Comput. Biol. Med. 37, 486–498 (2007)

    CrossRef  Google Scholar 

  5. Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4–5), 198–211 (2007)

    CrossRef  Google Scholar 

  6. Ferlay, J., Shin, H.R., Bray, F., Forman, D., Mathers, C., Parkin, D.M.: GLOBOCAN 2012: estimated cancer incidence, mortality and prevalence worldwide (2012). http://globocan.iarc.fr

  7. Gordon, G.J.: Translation of microarray data into clinically relevant cancer diagnostic tests using Gege expression ratios in lung cancer and mesothelioma. Cancer Res. 62, 4963–4967 (2002)

    Google Scholar 

  8. Hua, K.-L., Hsu, C.-H., Hidayati, S.C., Cheng, W.-H., Chen, Y.-J.: Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets Ther. 8, 2015–2022 (2015)

    Google Scholar 

  9. Ko, J.P., Rusinek, H., Jacobs, E.L., Babb, J.S., Betke, M., McGuinness, G., Naidich, D.P.: Small pulmonary nodules: volume measurement at chest CT-phantom study. Radiology 228, 864–870 (2003)

    CrossRef  Google Scholar 

  10. Kohad, R., Ahire, V.: Application of machine learning techniques for the diagnosis of lung cancer with ANT colony optimization. Int. J. Comput. Appl. 113(18), 34–41 (2015)

    Google Scholar 

  11. Kumar, D., Wong, A., Clausi, D.: Lung nodule classification using deep features in CT images. In: 12th Conference on Computer and Robot Vision (CRV), Halifax, NS, June 2015

    Google Scholar 

  12. Kumar, S., Moni, R., Rajeesh, J.: An automatic computer-aided diagnosis system for liver tumours on computed tomography images. Comput. Electr. Eng. 39, 1516–1526 (2013)

    CrossRef  Google Scholar 

  13. Li, F.: Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization. Radiology 237(2), 684–690 (2005)

    CrossRef  Google Scholar 

  14. Orozco, H.M., et al.: Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine. Biomed. Eng. 14(1), 9–17 (2015)

    Google Scholar 

  15. Reeves, A.P., Chan, A.B., Yankelevitz, D.F., Henschke, C.I., Kressler, B., Kostis, W.J.: On measuring the change in size of pulmonary nodules. IEEE Trans. Med. Imaging 25, 435–450 (2006)

    CrossRef  Google Scholar 

  16. Sergeeva, M., Ryabchikov, I., Glaznev, M., Gusarova, N.: Classification of pulmonary nodules on computed tomography scans. Evaluation of the effectiveness of application of textural features extracted using wavelet transform of image. In: FRUCT 2016, 18–22 April 2016 (in print)

    Google Scholar 

  17. Valente, I.R.S., et al.: Automatic 3D pulmonary nodule detection in CT images: a survey. Comput. Meth. Prog. Biomed. 124, 91–107 (2015)

    CrossRef  Google Scholar 

  18. Wiemker, R., Zwartkruis, A.: Optimal thresholding for 3d segmentation of pulmonary nodules in high resolution CT. In: Lemke, H.U., Vannier, M.W., Inamura, K., Farman, A.G., Doi, K. (eds.) Proceedings of the 15th International Congress and Exhibition on Computer Assisted Radiology and Surgery, CARS 2001, 27–30 June, pp. 653–658. Elsevier, Berlin (2001)

    Google Scholar 

  19. Wormanns, D., Diederich, S.: Characterization of small pulmonary nodules by CT. Eur. Radiol. 14, 1380–1391 (2004)

    Google Scholar 

  20. Yuan, R., Vos, P.M., Cooperberg, P.L.: Computer-aided detection in screening CT for pulmonary nodules. Am. J. Roentgenol. 186(5), 1280–1287 (2006)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Galina Artemova .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Artemova, G., Gusarova, N., Dobrenko, N., Trofimov, V., Vatian, A. (2016). Analysis of the Classification Methods of Cancer Types by Computer Tomography Images. In: Chugunov, A., Bolgov, R., Kabanov, Y., Kampis, G., Wimmer, M. (eds) Digital Transformation and Global Society. DTGS 2016. Communications in Computer and Information Science, vol 674. Springer, Cham. https://doi.org/10.1007/978-3-319-49700-6_52

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-49700-6_52

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49699-3

  • Online ISBN: 978-3-319-49700-6

  • eBook Packages: Computer ScienceComputer Science (R0)