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On the estimation of the number of fuzzy sets for fuzzy rule-based classification systems
<span title="">2011</span>
<i title="IEEE">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/srpj7mhbunarbj7r3eevxdit3y" style="color: black;">2011 11th International Conference on Hybrid Intelligent Systems (HIS)</a>
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Tissue classification has become more sophisticated in medical domain where the classification of tissues are performed with the spectra meter. There are many approaches has been discussed for the development of tissue classification, but struggles with the problem of less accuracy and false classification ratio. We propose a multi attribute light absorption estimation technique using fuzzy rule set. The method passes the light from the spectra meter over the tissue and the skin reflects
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/his.2011.6122107">doi:10.1109/his.2011.6122107</a>
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... amount of light reflected is identified. Also the method computes the area of the tissue and the area of light deployment, the strength of light rays being received. With these features the method computes the region based light absorption by computing the light being received or reflected at each section or region of the tissue image. Using the computed multi attribute sectional light absorption values, the method generates number of rules to perform image classification. At the training phase, the method segments the input set image and for each class of image the method extracts the area of diseased skin, the amount of light being applied and the strength of light being received and so on. The rule set is generated from these data and matched with the input feature vector to compute the similarity which the tissue classification is performed.
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