Skip to main content
SpringerLink
Log in

Information-Extreme Learning Algorithm for a System of Recognition of Morphological Images in Diagnosing Oncological Pathologies

  • Published:
Cybernetics and Systems Analysis Aims and scope

Abstract

This paper considers the optimization of a set of brightness gradations for pixel images of the tissue morphology of patients with oncological pathology. The influence of a set of pixel brightness gradations on the functional performance of training a system for the recognition of images of oncological diseases is analyzed. It is established that a change in the collection of pixel brightnesses in the receptive field increases the value of the criterion of functional efficiency and, as a result, the reliability of recognition.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. S. V. Ablameyko and A. M. Nedzved, Processing of Optical Images of Cellular Structures in Medicine [in Russian], State United Institute of Informatics Problems of NAS of Belarus, Minsk (2005).

    Google Scholar 

  2. A. Belotserkovsky, A. Nedzved, S. Ablameyko, I. Gurevich, and O. Salvetti, “Automation of preliminary histological diagnostic of oncological diseases,” in: Proc. Intern. Conf. on Advanced Information and Telemedicine Technologies for Health (November 8–10, 2005), 1, Minsk, Belarus, United Institute of Informatics Problems of the National Academy of Sciences of Belarus, Minsk (2005), pp. 70–74.

  3. K. N. Malyshevska, “Segmentation of irregular regions in medical images with the help of Kohonen cards,” Visnyk Cherkas. Derzh. Technolog. Univ-ta, No. 4, 32–35 (2010).

  4. A. S. Krasnopoyasovsky, Information Synthesis of Intelligent Control Systems: An Approach Based on the Method of Functional-Statistical Tests [in Ukrainian], Vyd-vo SumDU, Sumy (2004).

    Google Scholar 

  5. A. S. Dovbysh, Foundations of Intelligent Systems Design [in Ukrainian], Vyd-vo SumDU, Sumy (2009).

    Google Scholar 

  6. A. S. Dovbysh, S. S. Martynenko, A. S. Kovalenko, and N. N. Budnyk, “Information-extreme algorithm for recognizing current distribution maps in magnetocardiography,” J. Automat. and Inform. Sci., 43, No. 2, 63–70 (2011).

    Article  Google Scholar 

  7. I. V. Shelekhov and M. S. Rudenko, “Information-extreme algorithm for the optimization of threshold brightness parameters of morphological images in diagnosing oncopathologies,” Radioelectronic and Computer Systems, No 3(55) 94–100 (2012).

    Google Scholar 

  8. A. S. Dovbysh, A. M. Romanyuk, and M. S. Rudenko, “Frame identification in problems of recognition of images of medical and biological objects,” Bionics of Intelligence, No. 1 (78), 53–58 (2012).

Download references

Author information

Authors and Affiliations

  1. Sumy State University,, Sumy, Ukraine

    A. S. Dovbysh & M. S. Rudenko

Authors
  1. A. S. Dovbysh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. S. Rudenko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. S. Dovbysh.

Additional information

Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 178–184, January–February 2014.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dovbysh, A.S., Rudenko, M.S. Information-Extreme Learning Algorithm for a System of Recognition of Morphological Images in Diagnosing Oncological Pathologies. Cybern Syst Anal 50, 157–162 (2014). https://doi.org/10.1007/s10559-014-9603-y

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10559-014-9603-y

Keywords

Search

Navigation