Breast Cancer Classification Using Fuzzy Central Moments

  • H. D. Cheng
  • Y. G. Hu
  • D. L. Hung
  • C. Y. Wu
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 83)


Breast cancer continues to be one of the most deadly diseases among American women, which is the second leading cause of cancer-related mortality among American women. Currently there are more than 50 million women over the age of 40 at risk of breast cancer and approximately 144,000 new cases of breast cancer are expected each year in the United States. One out of eight women will develop breast cancer at some point during her lifetime in this country [1,2]. Because of the high incidence of breast cancer, any improvement in the process of diagnosing the disease may have a significant impact on saving lives and cutting costs in the health care system. Since the cause of breast cancer remains unknown and the earlier stage tumors can be more easily and less expensively treated, early detection is the key to breast cancer control. Mammography has proven to be the most reliable method and the major diagnosis means for detecting and classifying breast cancer in the early stage. Studies have shown a decrease in both severe breast cancer and mortality in women who undergo regular mammographic screens [3].


Breast Cancer Gray Level Markov Random Field Fuzzy Entropy Fuzzy Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • H. D. Cheng
    • 1
  • Y. G. Hu
    • 1
  • D. L. Hung
    • 2
  • C. Y. Wu
    • 1
  1. 1.Department of Computer ScienceUtah State UniversityLoganUSA
  2. 2.Department of Computer, Information and Systems EngineeringSan Jose State UniversitySan JoseUSA

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