Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Medical image diagnosis of lung cancer by multi-layered GMDH-type neural network self-selecting functions

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

In this study, a revised Group Method of Data Handling (GMDH)-type neural network self-selecting functions is applied to the computer aided image diagnosis (CAD) of lung cancer. The GMDH-type neural network algorithm has an ability of self-selecting optimum neural network architecture from three neural network architectures, such as sigmoid function neural network, radial basis function neural network and polynomial neural network. The GMDH-type neural network also has abilities of self-selecting the number of layers, the number of neurons in hidden layers and useful input variables. This algorithm is applied to CAD of lung cancers, and it is shown that this algorithm is useful for the CAD, and is very easy to apply to practical complex problems because optimum neural network architecture is automatically organized.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Stoitsis J, Valavanis J et al (2006) Computer aided diagnosis based on medical image processing and artificial intelligence methods. Nuclear Instrum Methods Phys Research A 569:591–595

    Article  Google Scholar 

  2. Doi K (2005) Current status and future potential of computer-aided diagnosis in medical imaging. Br J Radiol 78:S3–S19

    Article  Google Scholar 

  3. Mougiakakou SG, Valavanis LK et al (2007) Differential diagnosis of CT focal liver lesions using textures, feature selection and ensemble driven classifiers. Artif Intell Med 41:25–37

    Article  Google Scholar 

  4. Hung YL, Chen JH, Shen WC (2006) Diagnosis of hepatic tumors with texture analysis in nonenhanced computed tomography images. Acad Radiol 13(6):713–720

    Article  Google Scholar 

  5. Kondo T, Ueno J (2008) Multi-layered GMDH-type neural network self-selecting optimum neural network architecture and its application to 3-dimensional medical image recognition of blood vessels. Int J Innov Comput Inf Control 4(1):175–187

    Google Scholar 

  6. Kondo T (1998) GMDH neural network algorithm using the heuristic self-organization method and its application to the pattern identification problem. In: Proceedings of the 37th SICE Annual Conference, pp 1143–1148

  7. Farlow SJ (ed) (1984) Self-organizing methods in modeling, GMDH-type algorithm. Marcel Dekker Inc., New York

    Google Scholar 

  8. Ivakhnenko AG (1970) Heuristic self-organization in problems of engineering cybernetics. Automatica 6(2):207–219

    Article  MathSciNet  Google Scholar 

  9. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723

    Article  MathSciNet  MATH  Google Scholar 

  10. Tamura H, Kondo T (1980) Heuristics free group method of data handling algorithm of generating optimum partial polynomials with application to air pollution prediction. Int J Syst Sci 11(9):1095–1111

    Article  Google Scholar 

  11. Draper NR, Smith H (1981) Applied regression analysis. Wiley, New York

    MATH  Google Scholar 

Download references

Acknowledgments

This work was supported by (JSPS) KAKENHI 22560403.

Author information

Authors and Affiliations

  1. Graduate School of Health Sciences, The University of Tokushima, 3-18-15 Kuramoto-cho, Tokushima, 770-8509, Japan

    Tadashi Kondo, Junji Ueno & Shoichiro Takao

Authors
  1. Tadashi Kondo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junji Ueno
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shoichiro Takao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tadashi Kondo.

About this article

Cite this article

Kondo, T., Ueno, J. & Takao, S. Medical image diagnosis of lung cancer by multi-layered GMDH-type neural network self-selecting functions. Artif Life Robotics 18, 20–26 (2013). https://doi.org/10.1007/s10015-013-0094-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-013-0094-0

Keywords

Search

Navigation