Abstract
Breast cancer is the most commonly diagnosed form of cancer in women accounting for about 30% of all cases. Medical thermography has been shown to be well suited for the task of detecting breast cancer, in particular when the tumour is in its early stages or in dense tissue. In this paper we perform breast cancer analysis based on thermography. We employ a series of statistical features extracted from the thermograms which describe bilateral differences between left and right breast areas. These features then form the basis of a hybrid fuzzy rule-based classification system for diagnosis. The rule base of the classifier is optimised through the application of a genetic algorithm which ensures a small set of rules coupled with high classification performance. Experimental results on a large dataset of nearly 150 cases confirm the efficacy of our approach.
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Schaefer, G., Nakashima, T., Zavisek, M. (2008). Analysis of Breast Thermograms Based on Statistical Image Features and Hybrid Fuzzy Classification. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89639-5_72
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DOI: https://doi.org/10.1007/978-3-540-89639-5_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89638-8
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