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A novel fast fuzzy neural network backpropagation algorithm for colon cancer cell image discrimination

Lookup NU author(s): Ephraim Nwoye, Dr Li Khor, Professor Satnam Dlay, Dr Wai Lok Woo

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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. © Springer-Verlag Berlin Heidelberg 2006.


Publication metadata

Author(s): Nwoye E, Khor LC, Dlay SS, Woo WL

Editor(s): Wang, J; Yi, Z; Zurada, JM; Lu, B-L; Hujun, Y

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Advances in Neural Networks (ISNN): Third International Symposium on Neural Networks

Year of Conference: 2006

Number of Volumes: 3

Pages: 760-769

ISSN: 0302-9743 (Print) 1611-3349 (Online)

Publisher: Springer Berlin / Heidelberg

URL: http://dx.doi.org/10.1007/11760191_112

DOI: 10.1007/11760191_112

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science, v. 3973

ISBN: 9783540344827


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