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Comparison of two AI methods for colonic tissue image classification

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5 Author(s)

Analysis of tissue is essential in dealing with a number of problems in cancer research. The identification of normal, dysplastic and cancerous colonic mucosa is an example of such a problem. In this paper, texture analysis techniques have been employed with the purpose of measuring characteristics of the tissue images. Those include histogram, grey-level difference statistics and co-occurrence matrix feature extraction algorithms. These characteristics are used as inputs for two different artificial intelligence approaches to address the image classification problem; a genetic algorithm and an artificial neural network. No significant differences have been found in the classifications obtained by both methodologies.

Published in:

Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE  (Volume:2 )

Date of Conference:

17-21 Sept. 2003