JACIII Vol.18 No.5 pp. 792-797
doi: 10.20965/jaciii.2014.p0792


Implementation of Wavelets and Artificial Neural Networks in Colonic Histopathological Classification

Samantha Denise F. Hilado*1, Laurence A. Gan Lim*1,
Raouf N. G. Naguib*2, Elmer P. Dadios*3, and Jose Maria C. Avila*4

*1Mechanical Engineering Department, De La Salle University, Manila, 2401 Taft Ave., Manila 1004, Philippines

*2BIOCORE Research and Consultancy International (BIOCORE, Coventry University), Liverpool, United Kingdom

*3Manufacturing Engineering and Management Department, De La Salle University, Manila, 2401 Taft Ave., Manila 1004, Philippines

*4Department of Pathology, University of the Philippines, Manila, Philippines

February 26, 2014
May 7, 2014
September 20, 2014
colon cancer, medical image analysis, wavelet transform, artificial neural networks
Colon cancer is one type of cancer that has a high death rate, but early diagnosis can improve the chances of patient recovery. Computer-assisted diagnosis can aid in determining whether images are of healthy or cancerous tissues. This study aims to contribute to the automatic classification of microscopic colonic images by implementing a 2-D wavelet transform for feature extraction and neural networks for classification. The colonic histopathological images are assigned to either the normal, cancerous, or adenomatous polyp classes. The proposed algorithm is able to determine which of the three classes the images belong to at a 91.11% rate of accuracy.
Cite this article as:
S. Hilado, L. Lim, R. Naguib, E. Dadios, and J. Avila, “Implementation of Wavelets and Artificial Neural Networks in Colonic Histopathological Classification,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.5, pp. 792-797, 2014.
Data files:
  1. [1] WHO Cancer Mortality Database [Online],
    Available: [July 9, 2013]
  2. [2] Cancer Research UK. Bowel Cancer Statistics [Online],
    Available: [July 9, 2013]
  3. [3] World Health Organization. Fact Sheets. Cancer [Online],
    Available: [July 9, 2013]
  4. [4] L. A. Gan Lim, R. N. G. Naguib, E. P. Dadios, and J. M. C. Avila, “Analysis of colonic histopathological images using pixel intensities and Hough transform,” Philippine Science Letters, Vol.3, No.1, pp. 128-135, 2010.
  5. [5] M.N. Gurcan, et al., “Histopathological Image Analysis: A Review,” IEEE Reviews in Biomedical Engineering, Vol.2, pp. 147-171, 2009.
  6. [6] Y. Hong, L. Zeng-li, and H. Wei, “Research for the colon cancer based on the EMD and LS-SVM,” 2011 4th Int. Conf. on Intelligent Computation Technology and Automation, 2011.
  7. [7] K. M. Rajpoot and N. M. Rajpoot, “Wavelet Based Segmentation of Hyperspectral Colon Tissue Imagery,” Proc. IEEE INMIC 2003, pp. 38-43, 2003.
  8. [8] Y. Xu, J. Zhu, E. Chang, and Z. Tu, “Multiple Clustered Instance Learning for Histopathology Cancer Image Classification, Segmentation and Clustering” 2012 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 964-971, 2012.
  9. [9] D. Onder, S. Sarioglu, and B. Karacali, “Automated labelling of cancer textures in colorectal Histopathology slides using quasisupervised learning,” Micron, Vol.47, pp. 33-42, 2013.
  10. [10] L. A. Gan Lim, R. N. G. Naguib, E. P. Dadios, and J. M. C. Avila, “Implementation of GA-KSOM and ANFIS in the classification of colonic histopathological images,” TENCON 2012 - 2012 IEEE Region 10 Conf., Nov. 2012.
  11. [11] H. Kalkan, M. Nap, R. P.W. Duin, and M. Loog, “Automated Classification of Local Patches in Colon Histopathology,” 21st Int. Conf. on Pattern Recognition (ICPR 2012), pp. 61-64, Nov.11-15, 2012.
  12. [12] J. Filippas, H. Arochena, S. A. Amin, R. N. G. Naguib, and M. K. Bennett, “Comparison Of Two AI Methods For Colonic Tissue Image Classification,” Proc. of the 25th Annual Int. Conf. of the IEEE EMBS, pp. 1323-1326, 2003.
  13. [13] S.G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.11, No.7, July 1989.
  14. [14] C. Arizmendi, A. Vellido, and E. Romero, “Classification of human brain tumours from MRS data using Discrete Wavelet Transform and Bayesian Neural Networks,” Expert Systems with Applications, Vol.39, pp. 5223-5232, 2012.
  15. [15] W.Xu, L.Li and S. Zou, “Detection and Classification of Microcalcifications Based on DWT and ANFIS,” 1st Int. Conf. on Bioinformatics and Biomedical Engineering (ICBBE 2007), pp. 547-550, 2007.
  16. [16] J. S. Walker, “A Primer on Wavelets and Their Scientific Applications,” CRC Press, 1999.
  17. [17] A. Subasi, “Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction,” Computers in Biology and Medicine, Vol.37, pp. 227-244, 2007.
  18. [18] S. Arivazhagan and L. Ganesan, “Texture classification using wavelet transform,” Pattern Recognition Letters, Vol.24, pp. 1513-1521, 2003.
  19. [19] R. M. Haralick, K. Shanmugam, and I. Dinstein, “Texture features for image classification,” IEEE Trans. on Systems, Man, and Cybernetics (SMC), Vol.3, No.6, pp. 610-621, 1973.
  20. [20] R. Kozmaa, S. Bressler, L. Perlovsky, and G.K. Venayagamoorth, “Advances in neural networks research: An introduction,” Neural Networks, Vol.22, pp. 489-490, 2009.

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