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Detection of mass and circumscribed mass in mammograms employing radial-basis-function neural networks

Published: 26 February 2010 Publication History

Abstract

The proposed method detects the exact location of masses and circumscribed masses in mammograms based on RBFNN (Redial Basis Function Neural Network) with accuracy of 62% and 50% respectively for mammograms containing masses. The recognition rate for the normal one reaches 94.89% in MIAS (Mammography Image Analysis Society) database. Also the results are independent of preprocessing. This procedure is implemented by performing sub-image windowing analysis. The evaluation of the proposed mass and circumscribed mass detection was carried out in the MIAS database, giving reliable detection.

References

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D. Kopans, Breast imaging, Philadelphia, PA: J. B. Lippincott Company, 1989.
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I. Anderson, "Mammography in clinical practice", Med. Radiography and Photography, vol. 62, no. 2, p. 2, 1986.
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J. Martin, M. Moskowitz, and J. Milbrath, "Breast cancer missed by mammography", AJR, vol. 132, p. 737, 1979.
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M. Wallis, M. Walsh, and J. Lee, "A review of false negative mammography in a symptomatic population", Clin. Radiology, vol. 44, pp. 13--15, 1991.
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Z. Huo, M. Giger, C. Vyborny, D. Wolverton R. Schmidt and K. Doi, "Computer -- aided diagnosis: automated classification of mammographic mass lesions", in Digital Mammography"96.
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http://www.cancer.gov/cancertopics/wyntk/breast
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www.nbcc.org.au/pages/info/resource/nbccpubs-/bc21-94/intro.htm
[8]
http://www.oncolink.org/types/article.cfm
[9]
http://s20c.smb.man.ac.uk/services/MIASmini.html

Cited By

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  • (2018)Learning vector quantization inference classifier in breast abnormality classificationJournal of Intelligent & Fuzzy Systems10.3233/JIFS-16985035:6(6101-6116)Online publication date: 24-Dec-2018
  • (2017)Breast Cancer Detection Using Polynomial Fitting for Intensity Spreading Within ROIsProceedings of the International Conference on Advanced Intelligent Systems and Informatics 201710.1007/978-3-319-64861-3_12(129-139)Online publication date: 31-Aug-2017

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  1. Detection of mass and circumscribed mass in mammograms employing radial-basis-function neural networks

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      cover image ACM Other conferences
      ICWET '10: Proceedings of the International Conference and Workshop on Emerging Trends in Technology
      February 2010
      1070 pages
      ISBN:9781605588124
      DOI:10.1145/1741906
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      • UNITECH: Unitech Engineers, India
      • AICTE: All India Council for Technical Education

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      New York, NY, United States

      Publication History

      Published: 26 February 2010

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      Author Tags

      1. RBF (redial basis function)
      2. breast cancer
      3. kurtosis
      4. mammograms

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      • (2018)Learning vector quantization inference classifier in breast abnormality classificationJournal of Intelligent & Fuzzy Systems10.3233/JIFS-16985035:6(6101-6116)Online publication date: 24-Dec-2018
      • (2017)Breast Cancer Detection Using Polynomial Fitting for Intensity Spreading Within ROIsProceedings of the International Conference on Advanced Intelligent Systems and Informatics 201710.1007/978-3-319-64861-3_12(129-139)Online publication date: 31-Aug-2017

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