Urinary bladder tumor grade diagnosis using on-line trained neural networks

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2773 PART 1, Issue , 2003, Pages 199-206

  Tasoulis, D K ^a   Spyridonos, P ^a   Pavlidis, N G ^a   Cavouras, D ^b   Ravazoula, P ^c   Nikiforidis, G ^a   Vrahatis, M N ^a  

^a UNIVERSITY OF PATRAS   (Greece)

^b UNIVERSITY OF WEST ATTICA   (Greece)

^c UNIVERSITY HOSPITAL OF PATRAS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATION; CELLS; DIAGNOSIS; IMAGE PROCESSING; RESEARCH; TUMORS; KNOWLEDGE BASED SYSTEMS; NEURAL NETWORKS; STOCHASTIC SYSTEMS;

CELL NUCLEUS; CLASSIFIER SYSTEM; TUMORS GRADE; ANN CLASSIFIERS; AUTOMATED SYSTEMS; CLASSIFICATION ACCURACY; CLASSIFICATION SYSTEM; ONLINE METHODS; TRAINED NEURAL NETWORKS; URINARY BLADDER;

NEURAL NETWORKS; DIAGNOSIS;

EID: 8344289104     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-45224-9_29     Document Type: Conference Paper

References (13)

1. L. B. Almeida, T. Langlois, L. D. Amaral, and A. Plankhov. Parameter adaption in stohastic optimization. On-Line Learning in Neural Networks, pages 111-134, 1998.
2. N. Belacel and M. R. Boulassel. Multicriteria fuzzy assignment method: a useful tool to assist medical diagnosis. Artificial Intelligence in Medicine, 21:201-207, 2001.
3. H.-K. Choi, J. Vasko, E. Bengtsson, T. Jarkrans, U. Malmstrom, K. Wester, and C. Busch. Grading of transitional cell bladder carcinoma by texture analysis of histological sections. Analytical Cellular Pathology, 6:327-343, 1994.
4. C. De Prez, Y. De Launoit, R. Kiss, M. Petein, J.-L. Pasteels, and A. Verhest. Computerized morphonuclear cell image analysis of malignant disease in bladder tissues. Journal of Urology, 143:694-699, 1990.
5. K. Hornik. Multilayer feedforward networks are universal approximators. Neural Networks, 2:359-366, 1989.
6. T. Jarkrans, J. Vasko, E. Bengtsson, H.-K. Choi, U. Malmstrom, K. Wester, and C. Busch. Grading of transitional cell bladder carcinoma by image analysis of histological sections. Analytical Cellular Pathology, 18:135-158, 1995.
7. I. E. Jonathan, B. A. Mahul, R.R Victor, and F.K Mostofi. (transitional cell) neoplasms of the urinary bladder. The American Journal of Surgical Pathology, 22(12):1435-1448, 1998.
8. G. D. Magoulas, V. P. Plagianakos, and M. N. Vrahatis. Global learning rate adaptation in on-line neural network training. In Proceedings of the Second International Symposium in Neural Computation May 23-26, Berlin, Germany, 2000.
9. G. D. Magoulas, V. P. Plagianakos, and M. N. Vrahatis. Adaptive stepsize algorithms for on-line training of neural networks. Nonlinear Analysis, T.M.A., 47(5):3425-3430, 2001.
10. G. D. Magoulas, V. P. Plagianakos, and M. N. Vrahatis. Hybrid methods using evolutionary algorithms for on-line training. In INNS-IEEE International Joint Conference on Neural Networks (IJCNN), July 14-19, Washington, D.C., U.S.A., volume 3, pages 2218-2223, 2001.
11. G. D. Magoulas, V. P. Plagianakos, and M. N. Vrahatis. Improved neural network-based interpretation of colonoscopy images through on-line learning and evolution. In D. Dounias and D. A. Linkens, editors, European Network of Excellence on Intelligent Technologies for Smart Adaptive Systems, pages 38-43. 2001.
12. E. Ooms, W. Anderson, C. Alons, M. Boon, and R. Veldhuizen. Analysis of the performance of pathologists in grading of bladder tumors. Human Pathology, 14:140-143, 1983.
13. S. L. Parker, T. Tony, S. Bolden, and P. A. Wingo. Cancer statistics. Cancer Statistics 1997. CA Cancer J Clin, 47(5):5-27, 1997.