Combining fuzzy cognitive maps with support vector machines for bladder tumor grading

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4251 LNAI - I, Issue , 2006, Pages 515-523

  Papageorgiou, Elpiniki ^a   Georgoulas, George ^a   Stylios, Chrysostomos ^b   Nikiforidis, George ^a   Groumpos, Peter ^a  

^a UNIVERSITY OF PATRAS   (Greece)

^b Epirus Institute of Technology Arta   (Greece)

Author keywords

Bladder tumor grading; Fuzzy cognitive maps; Statistical learning; Support vector machine

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; FUZZY SETS; MATHEMATICAL MODELS; STATISTICAL METHODS; TUMORS;

BLADDER TUMOR GRADING; FUZZY COGNITIVE MAPS; STATISTICAL LEARNING; SUPPORT VECTOR MACHINE;

COGNITIVE SYSTEMS;

EID: 33750736025     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11892960_63     Document Type: Conference Paper

References (28)

1. Parker, S.L., Tony, T, Bolden, S, Wingo, P.A: Cancer Statistics, Cancer Statistics. CA Cancer J Clinics, 47 5 (1997) 5-27.
2. Ooms, E, Anderson, W, Alons C., Boon M, Veldhuizen R: "Analysis of the performance of pathologists in grading of bladder tumours", Human Pathology, 14 (1983) 140-143.
3. Gil, J, Wu, H, Wang, B.Y.: Image analysis and morphometry in the diagnosis of breast cancer, Microsc. Res. Techniq. 59 (2002) 109-118.
4. Catto J, Linkens D, Abbod M, Chen M, Burton J, Feeley K, Hamdy F: Artificial Intelligence in Predicting Bladder Cancer Outcome: A Comparison of Neuro-Fuzzy Modeling and Artificial Neural Networks. Artif Intell Med 9 (2003) 4172-4177.
5. Choi H, Vasko J, Bengtsson E, Jarkrans T, Malmstrom U, Wester K, Busch C.: Grading of transitional cell bladder carcinoma by texture analysis of histological sections. Anal Cell Pathol 6 (1994) 327-343.
6. Jarkrans T, Vasko J, Bengtsson E, Choi H, Malmstrom U, Wester K, Busch C. Grading of transitional cell bladder carcinoma by image analysis of histological sections. Anal Cell Pathol 18 (1995) 135-158.
7. Michie, D., Spiegelhalter, D. J., Taylor, C.C.: Machine Learning, Neural and Statistical Classification. Englewood Cliffs, N.J.: Prentice Hall (1994). Data available at http://www.ncc.up.pt/liacc/ML/statlog/datasets.html.
8. Podgorelec, V, Kokol, P, Stiglic, B, Rozman, I.: Decision Trees: An Overview and Their Use in Medicine, Journal of Medical Systems, 26 5 (2002) 445-463.
9. Dreiseit, S, Ohno-Machado, L, Kittler, H, Vinterbo, S, Billhardt, H, Binder, M: A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions, Journal of Biomedical Informatics 34 (2001) 28-36.
10. Antala, P., Fannesa, G., Timmermanb, D., Moreaua, Y, Moor B.D.: Bayesian applications of belief networks and multilayer perceptrons for ovarian tumor classification with rejection, Artificial Intelligence in Medicine 29 (2003) 39-60.
11. Stylios, C. D. and Groumpos, P.P.: Fuzzy Cognitive Maps in Modelling Supervisory Control Systems, J. of Intelligent & Fuzzy Systems, 8, (2000) 83-98.
12. Papageorgiou, E., Stylios, C., Groumpos, P: An Integrated Two-Level Hierarchical Decision Making System based on Fuzzy Cognitive Maps (FCMs), IEEE Transactions on Biomedical Engineering, 50 12 (2003) 1326-1339.
13. Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P: Active Hebbian Learning to Train Fuzzy Cognitive Maps, Int. J. Approx. Reasoning, 37 (2004) 219-249.
14. Papageorgiou, E.I., Groumpos, P.P.: A weight adaptation method for fine-tuning Fuzzy Cognitive Map causal links, Soft Computing Journal, 9 (2005) 846-857.
15. Papageorgiou, E.I., Spyridonos, P., Ravazoula, P., Stylios, C.D., Groumpos, P.P., Nikiforidis, G: Advanced Soft Computing Diagnosis Method for Tumor Grading, Artif Intell Med, 36 (2006) 59-70.
16. th Intern. Conf. on Artificial Intelligence and Soft Computing (ICAISC) Zakopane, Poland, 7-11 June, Lecture Notes in Computer Science 3070, (2004) 1031-1036.
17. Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, 2 2 (1998) 121-167.
18. Vapnik, V. N: The nature of statistical learning theory. New York: Springer-Verlag, 1995.
19. Muller, K. R., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B: An Introduction to Kernel-Based Learning Algorithms. IEEE Trans. Neural Networks, 2 2 (2001) 181-201.
20. Veropoulos, K., Cambell, C. and Cristianini, N.: Controlling the sensitivity of support machines, Proc. Int. Joint Conf. on Artificial Intelligence (IJCAI99), Stockholm, 1999. pp. 55-60.
21. Cristianini, N. and Shawe-Taylor, J: An Introduction to Support Vector Machines. Cambridge University Press, (2000).
22. Haykin, S: Neural Networks: A Comprehensive Foundation. 2nd ed. Englewood Cliffs, NJ: Prentice Hall, (1999).
23. Spyridonos, P, Ravazoula, P, Cavouras, D, Berberidis, K, Nikiforidis, G.: Computer-based grading of haematoxylin-eosin stained tissue sections of urinary bladder carcinomas, Med Inform & Intern Med, 26 3 (2001) 179-190.
24. Bostwick, G, Ramnani, D, Cheng, L.: Diagnosis and grading of bladder cancer and associated lesions, Urologic Clinics of North America, 26 (1999) 493-507.
25. Salzberg, S.L: On Comparing Classifiers: Pitfalls to avoid and a Recommended Approach, Data Mining and Knowledge Discovery, 1 (1997) 317-328.
26. Osuna, E.E., Freund, R., and Girosi, F: Support Vector Machines: Training and Applications, MIT, A.I. Memo No. 1602, March (1997).
27. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. 2nd ed. John Wiley & Sons, (2001).
28. Murphy, WM.: Urothelial neoplasia, Pathology and pathobiology of the urinary bladder and prostate, Baltimore, Williams & Wilkins, (1992).