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A Novel Fast Fuzzy Neural Network Backpropagation Algorithm for Colon Cancer Cell Image Discrimination

  • Ephram Nwoye
  • Li C. Khor
  • Satnam S. Dlay
  • Wai L. Woo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

In this paper a novel fast fuzzy backpropagation algorithm for classification of colon cell images is proposed. The experimental results show that the accuracy of the method is very high. The algorithm is evaluated using 116 cancer suspects and 88 normal colon cells images and results in a classification rate of 96.4%. The method automatically detects differences in biopsy images of the colorectal polyps, extracts the required image texture features and then classifies the cells into normal and cancer respectively. The net function computation is significantly faster. Convergence is quicker. It has an added advantage of being independent of the feature extraction procedure adopted, with knowledge and learning to overcome the sharpness of class characteristics associated with other classifiers algorithms. It can also be used to resolve a situation of in-between classes.

Keywords

Texture Feature Cancer Image Gray Level Cooccurrence Matrix Feature Extraction Procedure Inverse Difference Moment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ephram Nwoye
    • 1
  • Li C. Khor
    • 1
  • Satnam S. Dlay
    • 1
  • Wai L. Woo
    • 1
  1. 1.School of Electrical, Electronic and Computer EngineeringUniversity of Newcastle upon TyneUnited Kingdom

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