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RETRACTED ARTICLE: Mass classification method in mammograms using correlated association rule mining

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This article was retracted on 26 October 2015

Abstract

In this paper, we present an efficient computer-aided mass classification method in digitized mammograms using Association rule mining, which performs benign–malignant classification on region of interest that contains mass. One of the major mammographic characteristics for mass classification is texture. Association rule mining (ARM) exploits this important factor to classify the mass into benign or malignant. The statistical textural features used in characterizing the masses are mean, standard deviation, entropy, skewness, kurtosis and uniformity. The main aim of the method is to increase the effectiveness and efficiency of the classification process in an objective manner to reduce the numbers of false-positive of malignancies. Correlated association rule mining was proposed for classifying the marked regions into benign and malignant and 98.6% sensitivity and 97.4% specificity is achieved that is very much promising compare to the radiologist’s sensitivity 75%.

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Correspondence to Aswini Kumar Mohanty.

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An erratum to this article is available at http://dx.doi.org/10.1007/s00521-015-2084-8.

The Editor-in-Chief has decided to retract this article. Upon investigation carried out according to the Committee on Publication Ethics guidelines, it has been found that the authors have duplicated substantial parts from the following article: Classification Using Association Rules Rajanish Dass W.P. No. 2008-01-05 January 2008 INDIAN INSTITUTE OF MANAGEMENT AHMEDABAD INDIA

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Mohanty, A.K., Senapati, M., Beberta, S. et al. RETRACTED ARTICLE: Mass classification method in mammograms using correlated association rule mining. Neural Comput & Applic 23, 273–281 (2013). https://doi.org/10.1007/s00521-012-0857-x

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  • DOI: https://doi.org/10.1007/s00521-012-0857-x

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