Abstract
In this paper, we propose a CBR driven genetic algorithm to detect microcalcification clusters in digital mammograms towards computer-aided breast cancer screening. While being embedded inside the genetic algorithm, the CBR is performed as an “evaluator” and a “guide” in the proposed GA system. To form a base of cases, we adopted a competitive learning neural network to organize the MC feature vectors to construct the cases. Experiments are carried out under the DDSM database and the performances of the proposed algorithm are evaluated by the FROC curve, which show that the CBR driven genetic algorithm can achieve 98% accuracy at a low cost of false detection rate. Even in dense mammograms, the system can still detect the MCs correctly.
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References
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Heath, M., Bowyer, K.W., Kopans, D., et al.: Current status of the Digital Database for Screening Mammography. Digital Mammography, pp. 457–460. Kluwer Academic Publishers, Digital Mammography (1998)
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© 2004 Springer-Verlag Berlin Heidelberg
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Yao, B., Jiang, J., Peng, Y. (2004). A CBR Driven Genetic Algorithm for Microcalcification Cluster Detection. In: Motta, E., Shadbolt, N.R., Stutt, A., Gibbins, N. (eds) Engineering Knowledge in the Age of the Semantic Web. EKAW 2004. Lecture Notes in Computer Science(), vol 3257. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30202-5_43
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DOI: https://doi.org/10.1007/978-3-540-30202-5_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23340-4
Online ISBN: 978-3-540-30202-5
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