Classification of Prostate Cell Nuclei using Artificial Neural Network Methods
The purpose of this paper is to assess the value of neural networks for classification of cancer and noncancer prostate cells. Gauss Markov Random Fields, Fourier entropy and wavelet average deviation features are calculated from 80 noncancer and 80 cancer prostate cell nuclei. For classification, artificial neural network techniques which are multilayer perceptron, radial basis function and learning vector quantization are used. Two methods are utilized for multilayer perceptron. First method has single hidden layer and between 3-15 nodes, second method has two hidden layer and each layer has between 3-15 nodes. Overall classification rate of 86.88% is achieved.
[1] U. Schenck, and W. Planding, "Quantitation of visual screening
technique in cytology," in Proc. Image Analysis in Medicine, II.
National Symposium, pp. 7-14, 1998.
[2] D. Kopec, M.H. Kabir, D. Reinharth, O. Rothschild, and J.A.
Castiglione, "Human Errors in Medical Practice: Systematic
Classification and Reduction with Automated Information Systems," J.
of Medical Systems, vol. 27, no. 4, pp. 297-313, Aug. 2003.
[3] B. Djavan, M. Remzi, A. Zlotta, C. Seitz, P. Snow, and M. Marberger,
"Novel Artificial Neural Network for Early Detection of Prostate
Cancer", Journal of Clinical Oncology, vol. 20, no. 4, pp. 921-929 Feb.
2002.
[4] R.N.G. Naguib, and F.C. Hamdy, "Prognostic neuroclassification of
prostate cancer patients", in Proc. 19th Int. Conf. IEEE/EMBS, Chicago,
1997, pp. 1003-1006.
[5] W. Gnadt, D. Manolakis, E. Feleppa, F. Lizzi, T. Liu, P. Lee,
"Classification of prostate tissue using neural networks" in IJCNN '99.
vol. 5, July 1999, pp.3569 - 3572.
[6] S. Chatterjee, "Classification of natural textures using Gaussian Markov
random field models,", pp. 159-177, Markov Random Fields, Theory
and Applications, Chellappa, R., Jain, A., (ed.), Academic Press, 1991.
[7] B.S. Manjunath, R. Chellappa, "Unsupervised texture segmentation
using Markov random field models," IEEE Tran. Patt. Anal. Machine
Intel., vol. 13, pp. 478-482, 1991.
[8] M.E. Jernigan, F. D-Astous, "Entropy-based texture analysis in the
spatial frequency domain," IEEE Tran. Patt. Anal. Machine Intel., vol. 6,
pp. 237-243, 1984.
[9] G. Van de Wouwer, P. Scheunders, D. Van Dyck, "Statistical texture
characterization from discrete wavelet representation", IEEE Trans. On
Image Processing, vol. 8, pp. 592-598, 1999.
[10] I. Daubechies, Ten Lectures on Wavelets, Society for Industrial and
Applied Mathematics, 1992.p. 115-285.
[11] R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, 2nd ed., John
Wiley & Sons, Inc., 2001, ch. 6.
[12] S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed.,
Prentice Hall, 1999, ch. 3-5.
[13] C.M. Bishop, Neural Networks For Pattern Recognition, Oxford
University Press, 1995, ch. 3-5.
[14] T. Kohonen, Self-organization and Associative Memory, Springer-
Verlag, 1987.
[1] U. Schenck, and W. Planding, "Quantitation of visual screening
technique in cytology," in Proc. Image Analysis in Medicine, II.
National Symposium, pp. 7-14, 1998.
[2] D. Kopec, M.H. Kabir, D. Reinharth, O. Rothschild, and J.A.
Castiglione, "Human Errors in Medical Practice: Systematic
Classification and Reduction with Automated Information Systems," J.
of Medical Systems, vol. 27, no. 4, pp. 297-313, Aug. 2003.
[3] B. Djavan, M. Remzi, A. Zlotta, C. Seitz, P. Snow, and M. Marberger,
"Novel Artificial Neural Network for Early Detection of Prostate
Cancer", Journal of Clinical Oncology, vol. 20, no. 4, pp. 921-929 Feb.
2002.
[4] R.N.G. Naguib, and F.C. Hamdy, "Prognostic neuroclassification of
prostate cancer patients", in Proc. 19th Int. Conf. IEEE/EMBS, Chicago,
1997, pp. 1003-1006.
[5] W. Gnadt, D. Manolakis, E. Feleppa, F. Lizzi, T. Liu, P. Lee,
"Classification of prostate tissue using neural networks" in IJCNN '99.
vol. 5, July 1999, pp.3569 - 3572.
[6] S. Chatterjee, "Classification of natural textures using Gaussian Markov
random field models,", pp. 159-177, Markov Random Fields, Theory
and Applications, Chellappa, R., Jain, A., (ed.), Academic Press, 1991.
[7] B.S. Manjunath, R. Chellappa, "Unsupervised texture segmentation
using Markov random field models," IEEE Tran. Patt. Anal. Machine
Intel., vol. 13, pp. 478-482, 1991.
[8] M.E. Jernigan, F. D-Astous, "Entropy-based texture analysis in the
spatial frequency domain," IEEE Tran. Patt. Anal. Machine Intel., vol. 6,
pp. 237-243, 1984.
[9] G. Van de Wouwer, P. Scheunders, D. Van Dyck, "Statistical texture
characterization from discrete wavelet representation", IEEE Trans. On
Image Processing, vol. 8, pp. 592-598, 1999.
[10] I. Daubechies, Ten Lectures on Wavelets, Society for Industrial and
Applied Mathematics, 1992.p. 115-285.
[11] R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, 2nd ed., John
Wiley & Sons, Inc., 2001, ch. 6.
[12] S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed.,
Prentice Hall, 1999, ch. 3-5.
[13] C.M. Bishop, Neural Networks For Pattern Recognition, Oxford
University Press, 1995, ch. 3-5.
[14] T. Kohonen, Self-organization and Associative Memory, Springer-
Verlag, 1987.
@article{"International Journal of Medical, Medicine and Health Sciences:63915", author = "M. Sinecen and M. Makinacı", title = "Classification of Prostate Cell Nuclei using Artificial Neural Network Methods", abstract = "The purpose of this paper is to assess the value of neural networks for classification of cancer and noncancer prostate cells. Gauss Markov Random Fields, Fourier entropy and wavelet average deviation features are calculated from 80 noncancer and 80 cancer prostate cell nuclei. For classification, artificial neural network techniques which are multilayer perceptron, radial basis function and learning vector quantization are used. Two methods are utilized for multilayer perceptron. First method has single hidden layer and between 3-15 nodes, second method has two hidden layer and each layer has between 3-15 nodes. Overall classification rate of 86.88% is achieved.
", keywords = "Artificial neural networks, texture classification, cancer diagnosis.", volume = "1", number = "7", pages = "463-3", }
