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

Medical image diagnosis of liver cancer by hybrid feedback GMDH-type neural network using principal component-regression analysis

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

Abstract

The hybrid feedback group method of data handling (GMDH)-type neural network is proposed and applied to the medical image diagnosis of liver cancer. In this algorithm, the principal component-regression analysis is used for the learning calculation of the neural network, and the accurate and stable predicted values are obtained. Furthermore, this neural network has the feedback loop and the complexity of neural network architecture is increased using the feedback loop calculations. The neural network architecture is automatically organized so as to fit the complexity of the nonlinear system using the prediction error criterion defined as Akaike’s information criterion (AIC) or prediction sum of squares (PSS). The recognition results show that the hybrid feedback GMDH-type neural network algorithm is useful for the medical image diagnosis of liver cancer since the 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

Similar content being viewed by others

Explore related subjects

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

References

  1. 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 

  2. 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

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

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  6. 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. System Sci. 11(9):1095–1111

    Article  Google Scholar 

  7. Allen DN (1974) The relationship between variable selection and data augmentation and a method for prediction. Technometrics 16(1):125–126

    Article  MATH  MathSciNet  Google Scholar 

  8. Okuno T et al (1976) Multivariate Analysis, continued (in Japanese). Nikka-Giren, Tokyo

    Google Scholar 

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

    MATH  Google Scholar 

Download references

Acknowledgments

This work was supported by (JSPS) KAKENHI 15K06145.

Author information

Authors and Affiliations

  1. Graduate School of Health Sciences, Tokushima University, 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.

Additional information

This work was presented in part at the 20th International Symposium on Artificial Life and Robotics, Beppu, Oita, January 21–23, 2015.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kondo, T., Ueno, J. & Takao, S. Medical image diagnosis of liver cancer by hybrid feedback GMDH-type neural network using principal component-regression analysis. Artif Life Robotics 20, 145–151 (2015). https://doi.org/10.1007/s10015-015-0213-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-015-0213-1

Keywords

Search

Navigation