Fuzzy-rough methods for mammographic data analysis


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dc.contributor.author Shen, Qiang
dc.contributor.author Mac Parthaláin, Neil
dc.contributor.author Jensen, Richard
dc.date.accessioned 2008-09-29T13:40:27Z
dc.date.available 2008-09-29T13:40:27Z
dc.date.issued 2008-09-29
dc.identifier.citation Shen , Q , Mac Parthaláin , N & Jensen , R 2008 , ' Fuzzy-rough methods for mammographic data analysis ' . en
dc.identifier.other PURE: 86512
dc.identifier.other dspace: 2160/661
dc.identifier.uri http://hdl.handle.net/2160/661
dc.description N. Mac Parthalain, R. Jensen and Q. Shen. Rough and fuzzy-rough methods for mammographic data analysis. Proceedings of the 8th Annual UK Workshop on Computational Intelligence (UKCI'08), 2008. en
dc.description.abstract The accuracy of methods for the detection of mammographic abnormaility is heavily related to breast tissue characteristics. A breast with high tissue density will have reduced sensitivity in terms of detection. Also, breast tissue density is an important indicator of the risk of development of breast cancer. This paper investigates the application of a number of rough set and fuzzy-rough set techniques to mammographic image data. The aim is to attempt to automate the breast tissue classification procedure based on the consensus data of experts. The results of applying the various previously mentioned techniques show that they perform well, achieving high levels of classification accuracy. en
dc.language.iso eng
dc.title Fuzzy-rough methods for mammographic data analysis en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.description.status Non peer reviewed en

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