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A novel image mining technique for classification of mammograms using hybrid feature selection

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Abstract

The image mining technique deals with the extraction of implicit knowledge and image with data relationship or other patterns not explicitly stored in the images. It is an extension of data mining to image domain. The main objective of this paper is to apply image mining in the domain such as breast mammograms to classify and detect the cancerous tissue. Mammogram image can be classified into normal, benign, and malignant class. Total of 26 features including histogram intensity features and gray-level co-occurrence matrix features are extracted from mammogram images. A hybrid approach of feature selection is proposed, which approximately reduces 75% of the features, and new decision tree is used for classification. The most interesting one is that branch and bound algorithm that is used for feature selection provides the best optimal features and no where it is applied or used for gray-level co-occurrence matrix feature selection from mammogram. Experiments have been taken for a data set of 300 images taken from MIAS of different types with the aim of improving the accuracy by generating minimum number of rules to cover more patterns. The accuracy obtained by this method is approximately 97.7%, which is highly encouraging.

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Notes

  1. http://peipa.essex.ac.uk/info/mias.html.

  2. http://www.walkato.ac.nz/ml/weak.

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Authors and Affiliations

  1. SOA University, Bhubaneswar, Orissa, India

    Aswini Kumar Mohanty

  2. Department of Computer Science, Krupajal Engineering College, Bhubaneswar, 752 002, Orissa, India

    Manas Ranjan Senapati

  3. Department of Computer Science, Mody University, Lakshmangarh, 332311, Rajasthan, India

    Saroj Kumar Lenka

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  1. Aswini Kumar Mohanty
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  2. Manas Ranjan Senapati
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  3. Saroj Kumar Lenka
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Correspondence to Aswini Kumar Mohanty.

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Mohanty, A.K., Senapati, M.R. & Lenka, S.K. A novel image mining technique for classification of mammograms using hybrid feature selection. Neural Comput & Applic 22, 1151–1161 (2013). https://doi.org/10.1007/s00521-012-0881-x

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  • DOI: https://doi.org/10.1007/s00521-012-0881-x

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