Skip to main content

Advertisement

Log in

An ANNs-based system for the diagnosis and treatment of diseases

  • Published:
Neural Processing Letters 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

The review of a prototype Medical Decision Making System based on a robust configuration of Artificial Neural Networks (ANNs) is the topic of this article. It is designed to cover a whole category of human diseases, as can be proved by the already adapted systems that covered Pulmonogical and Haematological Cases. Moreover, an inside view is provided on one of the most crucial topics: ANNs' learning procedure.

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

Access this article

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.

Similar content being viewed by others

Explore related subjects

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

References

  1. W. C. House.Decision support systems: a databased, model-oriented, user-development discipline, Petrocelli Books Inc., Mc Graw Hill, 1991.

    Google Scholar 

  2. A. P. Dhawan. An expert system for the early detection of melanoma using knowledge-based image analysis,Anal. Quant. Cyt. and Hist., vol. 10, pp. 405–416, 1988.

    Google Scholar 

  3. G. A. Gorry. Computer-assisted clinical decisionmaking,Meth. Inf. Med., vol. 15, pp. 45–51, 1973.

    Google Scholar 

  4. E. H. Shortliffe, et al. Knowledge engineering for medical decision making: a review of computer-based clinical decision aids,Proc. of the IEEE, vol. 67, pp. 1207–1224, 1979.

    Google Scholar 

  5. P. Szolovits, S. G. Pauker. Categorical and probabilistic reasoning in medical diagnosis.AI, vol. 11, pp. 115–144, 1978.

    Google Scholar 

  6. A. Durg, et al. Identification of variegating coloring in skin tumors: NN vs. rule-based induction methods,IEEE Eng. in Med. and Biol., vol. 12, pp. 71–74 & 98, 1993.

    Google Scholar 

  7. K. Henson-Mack, et al. Integrating probabilistic and rule-based systems for CDD,Proc. IEEE SOUTHEASTCON (Birmingham), vol. II, pp. 699–702.

  8. G. Hripcsak. Problem-solving using neural networks,MD Comp., vol. 5, pp. 25–37, 1988.

    Google Scholar 

  9. P. S. Maclin, et al. Using neural networks to diagnose cancer,J. of Med. Sys., vol. 15, pp. 11–19, 1991.

    Google Scholar 

  10. B. H. Mulsant. A NN as an approach to clinical diagnosis,MD Comp., vol. 7, pp. 25–36, 1990.

    Google Scholar 

  11. T. J. O' Leary, et al. Computer-assisted image interpretation: use of a NN to differentiate tubular carcinoma from sclerosing adenosis,Mod. Path., vol. 5, pp. 402–405, 1992.

    Google Scholar 

  12. R. Poli, et al. An NN expert system for diagnosing and treating hypertension,IEEE Computer, vol. 24, pp. 64–71, 1991.

    Google Scholar 

  13. J. A. Reggia. Artificial neural systems in medical science and practice,MD Comp., vol. 5, pp. 4–6, 1988.

    Google Scholar 

  14. G.-P. K. Economou, et al. Medical decision making systems in pulmonology: a creative environment based on artificial neural networks,Proc. IEEE Int. Conf. on SMC (San Antonio), vol I, pp. 975–980.

  15. -. FPGA implementation of artificial neural networks: an application on medical expert systems,Proc. 4th Int. Conf. on Microelectronics for NN and Fuz. Sys. (Torino), vol. I, pp. 287–293.

  16. -. Suggesting diagnosis of diseases and treatment: how far artificial neural networks can go?,Proc. IEEE Int. Symp. on Artificial Neural Networks (Tainan), December 1994.

  17. D. R. Hush, B. G. Horne. Progress in supervised NN,IEEE Sig. Proc. Mag., vol. 10, pp. 8–39, 1993.

    Google Scholar 

  18. R. P. Lippmann. An introduction to computing with NN,IEEE ASSP Mag., vol. 4, pp. 4–22, 1987.

    Google Scholar 

  19. R. S. Scalero, N. Tepedelenlioglu. A fast new algorithm for training feedforward NN,IEEE Trans. on Sig. Proc., vol. 40, pp. 202–210, 1992.

    Google Scholar 

  20. A. Hart, J. Wyatt. Evaluating black-boxes as medical decision aids: issues arising from a study of NN,Med. Inf., vol. 15, pp. 229–236, 1990.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Economou, G.P.K., Lymberopoulos, D. & Goutis, C.E. An ANNs-based system for the diagnosis and treatment of diseases. Neural Process Lett 2, 22–26 (1995). https://doi.org/10.1007/BF02312379

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02312379

Keywords

Navigation