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Title: Thermography based breast cancer analysis using statistical features and fuzzy classifications
Authors: Schaefer, Gerald
Zavisek, Michal
Nakashima, Tomoharu
Keywords: Cancer diagnosis
Breast cancer
Medical thermography
Image analysis
Pattern classification
Issue Date: 2009
Publisher: © Pattern Recognition Society. Published by Elsevier Ltd.
Citation: SCHAEFER, G., ZAVISEK, M. and NAKASHIMA, T., 2009. Thermography based breast cancer analysis using statistical features and fuzzy classifications. Pattern Recognition, 42 (6), pp. 1133 - 1137.
Abstract: Medical thermography has proved to be useful in various medical applications including the detection of breast cancer where it is able to identify the local temperature increase caused by the high metabolic activity of cancer cells. It has been shown to be particularly well suited for picking up tumours in their early stages or tumours in dense tissue and outperforms other modalities such as mammography for these cases. In this paper we perform breast cancer analysis based on thermography, using a series of statistical features extracted from the thermograms quantifying the bilateral differences between left and right breast areas, coupled with a fuzzy rule-based classification system for diagnosis. Experimental results on a large dataset of nearly 150 cases confirm the efficacy of our approach that provides a classification accuracy of about 80%.
Description: This article was published in the journal, Pattern Recognition [© Pattern Recognition Society. Published by Elsevier Ltd.] and the definitive version is available at: http://dx.doi.org/10.1016/j.patcog.2008.08.007
Version: Accepted for publication
DOI: 10.1016/j.patcog.2008.08.007
URI: https://dspace.lboro.ac.uk/2134/14647
Publisher Link: http://dx.doi.org/10.1016/j.patcog.2008.08.007
ISSN: 0031-3203
Appears in Collections:Published Articles (Computer Science)

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