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Computer-aided diagnosis of breast cancer using bi-dimensional empirical mode decomposition

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Abstract

Breast cancer is a serious disease for women in the world and ranks the second cancer for women in many countries. Computer-aided diagnosis provides a second view to aid for radiologists to detect and diagnose breast cancer. In this paper, we present a novel approach of textural features extraction from mammograms using bi-dimensional empirical mode decomposition (BEMD) method and classification for diagnosis of breast cancer. Preprocessing techniques such as noise removal, artifacts and background suppression and contrast enhancement are performed before features extraction stage. Gray-level co-occurrence matrices-based features are extracted from 2-D intrinsic mode functions obtained by applying BEMD method on mammographic images. Finally, these features are given as an input to least squares support vector machine for classification of mammogram as normal or abnormal. The experimental results show that the proposed method achieves 95% accuracy, which is better than as compared to other published methods in the Mini-MIAS database for diagnosis of breast cancer. The proposed method can be used as automatic, accurate and noninvasive method for breast cancer diagnosis and treatment.

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Authors and Affiliations

  1. Discipline of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Jabalpur, 452005, India

    Varun Bajaj, Mayank Pawar, Vinod Kumar Meena & Mukesh Kumar

  2. Electrical and Electronics Engineering Department, Technology Faculty, Firat University, Elazig, Turkey

    Abdulkadir Sengur

  3. Department of Computer Science, University of Illinois at Springfield, Springfield, IL, USA

    Yanhui Guo

Authors
  1. Varun Bajaj
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  2. Mayank Pawar
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  3. Vinod Kumar Meena
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  4. Mukesh Kumar
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  5. Abdulkadir Sengur
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  6. Yanhui Guo
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Correspondence to Varun Bajaj.

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Bajaj, V., Pawar, M., Meena, V.K. et al. Computer-aided diagnosis of breast cancer using bi-dimensional empirical mode decomposition. Neural Comput & Applic 31, 3307–3315 (2019). https://doi.org/10.1007/s00521-017-3282-3

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  • DOI: https://doi.org/10.1007/s00521-017-3282-3

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