Evaluation and amelioration of computer-aided diagnosis with artificial neural networks utilizing small-sized sample sets

Biomedical Signal Processing and Control

Volumn 8, Issue 3, 2013, Pages 255-262

  Liu, K Y ^a   Smith, M R ^a   Fear, E C ^a   Rangayyan, R M ^a  

^a UNIVERSITY OF CALGARY   (Canada)

Author keywords

Artificial neural network (ANN) uncertainty; Bootstrap method; Computer aided disease diagnosis; Confidence interval (CI); Leave one out cross validation; Prediction interval (PI); Small sized sample set

Indexed keywords

COMPUTER AIDED NETWORK ANALYSIS; COMPUTER NETWORKS; DISTRIBUTION FUNCTIONS; NEURAL NETWORKS; SAMPLING;

BOOTSTRAP METHOD; CONFIDENCE INTERVAL; DISEASE DIAGNOSIS; LEAVE-ONE-OUT CROSS VALIDATIONS; PREDICTION INTERVAL; SAMPLE SETS;

COMPUTER AIDED DIAGNOSIS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BOOTSTRAPPING; COMPUTER ASSISTED DIAGNOSIS; CONFIDENCE INTERVAL; DIAGNOSTIC ACCURACY; DIAGNOSTIC VALUE; MAMMOGRAPHY; NERVE CELL; PREDICTION; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SAMPLE SIZE;

EID: 84875694763     PISSN: 17468094     EISSN: 17468108     Source Type: Journal    
DOI: 10.1016/j.bspc.2012.11.001     Document Type: Article

References (33)

1. C.E. Metz Basic principles of ROC analysis Seminars in Nuclear Medicine 8 1978 283 298
2. M.H. Zweig, and G. Campbell Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine Clinical Chemistry 39 1993 561 577
3. G. Papadopoulos, P.J. Edwards, and A.F. Murray Confidence estimation methods for neural networks: a practical comparison IEEE Transactions on Neural Networks 12 2001 1278 1287
4. B. Efron Bootstrap methods: another look at the Jackknife Annals of Statistics 7 1979 1 26
5. B. Efron, and R.J. Tibshirani An Introduction to the Bootstrap 1993 Chapman & Hall New York
6. S.S. Haykin Neural Networks and Learning Machines 3rd ed. 2009 Prentice Hall NJ, United States
7. Y. Liu, M.R. Smith, and R.M. Rangayyan The application of Efron's bootstrap methods in validating feature classification using artificial neural networks for the analysis of mammographic masses Proceedings of 26th International Conference on IEEE Engineering in Medicine and Biology Society San Francisco, CA, United States 2004 1553 1556
8. M.R. Chernick Bootstrap Methods: A Guide for Practitioners and Researchers 2nd ed. 2008 John Wiley & Sons, Inc. Hoboken, NJ
9. A.M. Zoubir, and D.R. Iskander Bootstrap Techniques for Signal Processing 2004 Cambridge University Press The Pitt Building, Trumpington Street, Cambridge, United Kingdom
10. N. Ueda, and R. Nakano Estimating expected error rates of neural network classifiers in small sample size situations: a comparison of cross-validation and bootstrap Proceedings of 1995 IEEE International Joint Conference on Neural Networks Perth, Australia 1995 101 104
11. G. Dupret, and M. Koda Bootstrap re-sampling for unbalanced data in supervised learning European Journal of Operational Research 134 2001 141 156
12. Y. Mitani, Y. Hamamoto, and S. Tomita Use of bootstrap samples in designing artificial neural network classifiers Proceedings of 1995 IEEE International Joint Conference on Neural Networks Perth, Australia 1995 2103 2106
13. A.M. Zoubir, and B. Boashash The bootstrap and its application in signal processing IEEE Signal Processing Magazine 15 1998 56 76
14. S. Lei, and M.R. Smith Evaluation of several nonparametric bootstrap methods to estimate confidence intervals for software metrics IEEE Transactions on Software Engineering 29 2003 996 1004
15. A.M. Zoubir, and D.R. Iskander Bootstrap methods and applications IEEE Signal Processing Magazine 24 2007 10 19
16. J. Carpenter, and J. Bithell Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians Statistics in Medicine 19 2000 1141 1164
17. M.R. Smith, X. Wang, and R.M. Rangayyan Evaluation of the sensitivity of a medical data-mining application to the number of elements in small databases Biomedical Signal Processing and Control 4 2009 262 268
18. S. Lee, M. Bolic, V.Z. Groza, H.R. Dajani, and S. Rajan Confidence interval estimation for oscillometric blood pressure measurements using bootstrap approaches IEEE Transactions on Instrumentation and Measurement 60 2011 3405 3415
19. T.C.S.S. André, and R.M. Rangayyan Classification of tumors and masses in mammograms using neural networks with shape and texture features Proceedings of 25th International Conference on IEEE Engineering in Medicine and Biology Society Cancun, Mexico 2003 2261 2264
20. H. Alto, R.M. Rangayyan, and J.E.L. Desautels Content-based retrieval and analysis of mammographic masses Journal of Electronic Imaging 14 2 2005 1 17 Article 023016. Erratum: 16 (1), (2007), p1, Article 019801
21. H. Alto, R.M. Rangayyan, R.B. Paranjape, J.E.L. Desautels, and H. Bryant An indexed atlas of digital mammograms for computer-aided diagnosis of breast cancer Annals of Telecommunications 58 2003 820 835
22. R.M. Rangayyan, N.R. Mudigonda, and J.E.L. Desautels Boundary modelling and shape analysis methods for classification of mammographic masses Medical and Biological Engineering and Computing 38 2000 487 496
23. T.C.S.S. André, and R.M. Rangayyan Classification of breast masses in mammograms using neural networks with shape, edge sharpness, and texture features Journal of Electronic Imaging 15 1 2006 1 10 Article 013019. Erratum: 16 (1), (2007) p1, Article 019802
24. G. Li, H. Alnuweiri, Y. Wu, and H. Li Acceleration of back propagation through initial weight pre-training with delta rule Proceedings of 1993 IEEE International Joint Conference on Neural Networks San Francisco, CA, United States 1993 580 585
25. Y. Lee, S.H. Oh, and M.W. Kim The effect of initial weights on premature saturation in back-propagation learning Proceedings of 1991 IEEE International Joint Conference on Neural Networks Seattle, WA, United States 1991 765 770
26. M.J.A. Berry, and G. Linoff Data Mining Techniques 2nd ed. 1997 John Wiley & Sons, Inc. New York
27. A. Blum Neural Networks in C++: An Object-Oriented Framework for Building Connectionist Systems 1992 John Wiley & Sons, Inc. New York
28. Z. Boger, and H. Guterman Knowledge extraction from artificial neural network models Proceedings of 1997 IEEE International Conference on Systems, Man, and Cybernetics vol. 3034, Orlando, FL, United States 1997 3030 3035
29. J. Heaton Introduction to Neural Networks for Java 2nd ed. 2008 Heaton Research Inc. Chesterfield, MO, United States
30. J. Yulei Uncertainty in the output of artificial neural networks IEEE Transactions on Medical Imaging 22 2003 913 921
31. C.E. Martin, S.K. Rogers, and D.W. Ruck Neural network Bayes error estimation Proceedings of 1994 IEEE International Joint Conference on Neural Networks vol. 301. Orlando, FL, United States 1994 305 308
32. A. Khosravi, S. Nahavandi, D. Creighton, and D. Srinivasan Optimizing the quality of bootstrap-based prediction intervals Proceedings of 2011 IEEE International Joint Conference on Neural Networks 2011 3072 3078
33. A. Khosravi, S. Nahavandi, and D. Creighton Developing optimal neural network metamodels based on prediction intervals Proceedings of 2009 International Joint Conference on Neural Networks 2009 1583 1589