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A novel feature selection framework based on grey wolf optimizer for mammogram image analysis

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Abstract

Breast cancer is one of the significant tumor death in women. Computer-aided diagnosis (CAD) supports the radiologists in recognizing the irregularities in an efficient manner. In this work, a novel CAD system proposed for mammogram image analysis based on grey wolf optimizer (GWO) with rough set theory. Texture, intensity, and shape-based features are extracted from mass segmented mammogram images. To derive the appropriate features from the extracted feature set, a novel dimensionality reduction algorithm is proposed based on GWO with rough set theory. GWO is a novel bio-inspired optimization algorithm, stimulated based on hunting activities and social hierarchy of the grey wolves. In this paper, a hybridization of GWO and Rough Set (GWORS) methods are used to find the significant features from the extracted mammogram images. To evaluate the effectiveness of the proposed GWORS, we compare it with other well-known rough set and bio-inspired feature selection algorithms including particle swarm optimize, genetic algorithm, Quick Reduct and Relative Reduct. From empirical results, it is observed that the proposed GWORS outperforms the other techniques in terms of accuracy, F-Measures and receiver operating characteristic curve.

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Acknowledgements

The authors would like to thank the Department of Science and Technology (DST), India for their financial assistance through the improvement of Science for Equity Empowerment and Development (SEED) programme (Grant No: SEED/WS/018/2015). The experimental analysis performed in Data Analytics and Solutions Lab (Catalyzed & Supported by SEED Division, DST, New Delhi), Sona College of Technology, Salem, Tamilnadu, India.

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Authors and Affiliations

  1. Department of Computer Sciences and Engineering, Sona College of Technology, Salem, India

    B. Sathiyabhama, S. Udhaya Kumar, J. Jayanthi, T. Sathiya, A. K. Ilavarasi & V. Yuvarajan

  2. Scientist, SEED Division, Department of Science and Technology, New Delhi, India

    Konga Gopikrishna

  3. Data Analytics and Solutions Lab, Sona College of Technology, Salem, India

    B. Sathiyabhama, S. Udhaya Kumar, J. Jayanthi, T. Sathiya, A. K. Ilavarasi & V. Yuvarajan

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  1. B. Sathiyabhama
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Correspondence to S. Udhaya Kumar.

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Sathiyabhama, B., Kumar, S.U., Jayanthi, J. et al. A novel feature selection framework based on grey wolf optimizer for mammogram image analysis. Neural Comput & Applic 33, 14583–14602 (2021). https://doi.org/10.1007/s00521-021-06099-z

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