Hybrid multi-layered GMDH-type neural network using principal component regression analysis and its application to medical image diagnosis of liver cancer

Procedia Computer Science

Volumn 22, Issue , 2013, Pages 172-181

  Kondo, Tadashi ^a   Ueno, Junji ^a   Takao, Shoichiro ^a  

^a UNIVERSITY OF TOKUSHIMA   (Japan)

Author keywords

Evolutionary computaion; GMDH; Liver cancer; Medical image diagnosis; Neural Network

Indexed keywords

EID: 84896964343     PISSN: 18770509     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/j.procs.2013.09.093     Document Type: Conference Paper

References (10)

1. T. Kondo and J. Ueno, "Multi-layered GMDH-type neural network self-selecting optimum neural network architecture and its application to 3-dimensional medical image recognition of blood vessels", International Journal of Innovative Computing, Information and Control, Vol. 4, no. 1, pp. 175-187, 2008.
2. th international symposium on Artificial Life and Robotics, No. GS21-4, pp. 1-4, 2007.
3. T. Kondo, J. Ueno, "Logistic GMDH-type neural network and its application to identification of X-ray film characteristic curve", Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 11, no. 3, pp. 312-318, 2007.
4. T. Kondo, "GMDH neural network algorithm using the heuristic self-organization method and its application to the pattern identification problem", Proc. of the 37th SICE Annual Conference, pp. 1143-1148, 1998.
5. S. J. Farlow ed., Self-organizing methods in modeling, GMDH-type algorithm, New York: Marcel Dekker Inc., 1984.
6. A. G. Ivakhnenko, "Heuristic self-organization in problems of engineering cybernetics", Automatica, vo. 6, no. 2, pp. 207-219, 1970.
7. H. Akaike, "A new look at the statistical model identification" , IEEE Trans. Automatic Control, vol. AC-19, no. 6, pp. 716-723, 1974.
8. H. Tamura, T. Kondo, "Heuristics free group method of data handling algorithm of generating optimum partial polynomials with application to air pollution prediction", Int. J. System Sci., Vol. 11, no. 9, pp. 1095-1 111, 1980.
9. T. Haga and S. Hasimoto, Regression analysis and principal component analysis, Tokyo: Union of Japanese Scientists and Engineers, 1986.
10. N. R. Draper, H. Smith, Applied Regression Analysis, New York: John Wiley and Sons, 1981.