Neural network-based colonoscopic diagnosis using on-line learning and differential evolution

Applied Soft Computing Journal

Volumn 4, Issue 4, 2004, Pages 369-379

  Magoulas, George D ^a   Plagianakos, Vassilis P ^b   Vrahatis, Michael N ^b  

^a UNIVERSITY OF LONDON   (United Kingdom)

^b UNIVERSITY OF PATRAS   (Greece)

Author keywords

Artificial evolution; Back propagation networks; Differential evolution strategies; Medical image interpretation; Minimally invasive imaging procedures; On line learning

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; COLONOSCOPY; COMPUTER ASSISTED DIAGNOSIS; COMPUTER MEMORY; DIAGNOSTIC ACCURACY; DIAGNOSTIC ERROR; DYNAMICS; ONLINE SYSTEM;

EID: 2942691391     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2004.01.005     Document Type: Article

References (35)

1. L.B. Almeida, T. Langlois, J.D. Amaral, A. Plankhov, Parameter adaptation in stochastic optimisation, in: D. Saad (Ed.), Proceedings of the On-line Learning in Neural Networks, Cambridge University Press, 1998, pp. 111-134.
2. P. Angeline, Tracking extrema in dynamic environments, in: Proceedings of the Sixth Annual Conference on Evolutionary Programming VI, Springer, 1997, pp. 335-345.
3. D. Anguita, Smart adaptive systems: state of the art and future directions of research, in: Proceedings of the European Symposium on Intelligent Technologies, Hybrid Systems and their Implementation on Smart Adaptive Systems-EUNITE 2001, Tenerife, Spain, 2001, pp. 1-4.
4. Battiti R. First- and second-order methods for learning: between steepest descent and Newton's method. Neural Comput. 4:1992;141-166
5. P.M. Delaney, G.D. Papworth, R.G. King, Fibre optic confocal imaging (FOCI) for in vivo subsurface microscopy of the colon, in: V.R. Preedy, R.R. Watson (Eds.), Methods in Disease: Investigating the Gastrointestinal Tract, Greenwich Medical Media, London, UK, 1998.
6. Doulamis A.D., Doulamis N.D., Kollias S.D. On-line retrainable neural networks: improving the performance of neural networks in image analysis problems. IEEE Trans. Neural Networks. 11:2000;137-155
7. Hagan M., Menhaj M. Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Networks. 5:1994;989-993
8. R. Hanka, T.P. Harte, A.K. Dixon, D.J. Lomas, P.D. Britton, Neural networks in the interpretation of contrast-enhanced magnetic resonance images of the breast, Healthcare Computing 1996, Harrogate, UK, 1996, pp. 275-283.
9. Haralick R.M. Statistical and structural approaches to texture. IEEE Proc. 67:1979;786-804
10. Innocent P.R., Barnes M., John R. Application of the fuzzy ART/MAP and MinMax/MAP neural network models to radiographic image classification. Artif. Intell. Med. 11:1997;241-263
11. Jacobs R. Increased rates of convergence through learning rate adaptation. Neural Networks. 1(4):1988;295-307
12. Karkanis S., Magoulas G.D., Theofanous N. Image recognition and neuronal networks: intelligent systems for the improvement of imaging information. Minim. Invasive Ther. Allied Technol. 9:2000;225-230
13. S.A. Karkanis, G.D. Magoulas, D.K. Iakovidis, D.A. Karras, D.E. Maroulis, Evaluation of textural feature extraction schemes for neural network-based interpretation of regions in medical images, in: Proceedings of IEEE International Conference on Image Processing, Thessaloniki, Greece, 7-10 October 2001.
14. Kudo S.E., Kashida H., Tamura T., Kogure E., Imai Y., Yamano H., Hart A.R. Colonoscopic diagnosis and management of nonpolypoid early colorectal cancer. World J. Surg. 24(9):2000;1081-1090
15. C.T. Leondes, Image processing and pattern recognition, Neural Network Systems Techniques and Applications, Series 5, Academic Press, 1998.
16. C.G. Looney, Pattern Recognition using Neural Networks, Oxford University Press, Oxford, UK, 1997.
17. G.D. Magoulas, M.N. Vrahatis, T.N. Grapsa, G.S. Androulakis, Neural network supervised training based on a dimension reducing method, in: S.W. Ellacot, J.C. Mason, I.J. Anderson (Eds.), Mathematics of Neural Networks: Models, Algorithms and Applications, Kluwer, 1997, pp. 245-249.
18. Magoulas G.D., Vrahatis M.N., Androulakis G.S. Improving the convergence of the backpropagation algorithm using learning rate adaptation methods. Neural Comput. 11:1999;1769-1796
19. Magoulas G.D., Plagianakos V.P., Vrahatis M.N. Adaptive stepsize algorithms for on-line training of neural networks. Nonlin. Anal.: Theor. Methods Appl. 47:2001;3425-3430
20. Möller M. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks. 6:1993;525-533
21. Nagata S., Tanaka S., Haruma K., Yoshihara M., Sumii K., Kajiyama G., Shimamoto F. Pit pattern diagnosis of early colorectal carcinoma by magnifying colonoscopy: clinical and histological implications. Int. J. Oncol. 16:2000;927-934
22. Phee S.J., Ng W.S., Chen I.M., Seow-Choen F., Davies B.L. Automation of colonoscopy part II: visual-control aspects. IEEE Eng. Med. Biol. 17(3):1998;81-88
23. V.P. Plagianakos, G.D. Magoulas, M.N. Vrahatis, Supervised training using global search methods, in: N. Hadjisavvas, P. Pardalos (Eds.), Advances in Convex Analysis and Global Optimisation, Noncovex Optimization and its Applications, vol. 54, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2001, pp. 421-432.
24. V.P. Plagianakos, G.D. Magoulas, M.N. Vrahatis, Learning rate adaptation in stochastic gradient descent, in: N. Hadjisavvas, P. Pardalos (Eds.), Advances in Convex Analysis and Global Optimisation, Noncovex Optimization and its Applications, vol. 54, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2001, pp. 433-444.
25. M. Riedmiller, H. Braun, A direct adaptive method for faster back-propagation learning: the Rprop algorithm, in: Proceedings of IEEE International Conference on Neural Networks, San Francisco, 1993, pp. 586-591.
26. Roehl N.M., Pedreira C.E. An online learning approach: a methodology for time varying applications. Neural Comput. Appl. 10:2001;101-107
27. D. Saad, On-line Learning in Neural Networks, Cambridge University Press, 1998.
28. R. Salomon, P. Eggenberger, Adaptation on the evolutionary time scale: a working hypothesis and basic experiments, in: Proceedings of the Third European Conference on Artificial Evolution (AE'97), Nimes, France, Lecture Notes in Computer Science, vol. 1363, Springer, 1998.
29. N.N. Schraudolph, Online local gain adaptation for multi-layer perceptrons, Technical Report, IDSIA-09-98, IDSIA, Lugano, Switzerland, 1998.
30. N.N. Schraudolph, Local gain adaptation in stochastic gradient descend, Technical Report, IDSIA-09-99, IDSIA, Lugano, Switzerland, 1999.
31. Storn R., Price K. Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11:1997;341-359
32. R.S. Sutton, Adapting bias by gradient descent: an incremental version of delta-bar-delta, in: Proceedings of the 10th National Conference on Artificial Intelligence, MIT Press, 1992, pp. 171-176.
33. R.S. Sutton, S.D. Whitehead, Online learning with random representations, in: Proceedings of the 10th International Conference on Machine Learning, Morgan Kaufmann, 1993, pp. 314-321.
34. F. Vavak, T.C. Fogarty, A comparative study of steady state and generational genetic algorithms, Evolutionary Computing: AISB Workshop, Lecture Notes in Computer Science, vol. 1143, Springer, 1996.
35. Vogl T.P., Mangis J.K., Rigler A.K., Zink W.T., Alkon D.L. Accelerating the convergence of the back-propagation method. Biol. Cybern. 59:1988;257-263