Comparison of two AI methods for colonic tissue image classification

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.