Publisher
Springer Science and Business Media LLC
Subject
Health Information Management,Health Informatics,Information Systems,Medicine (miscellaneous)
Reference17 articles.
1. World Health Organization (WHO),
http://www.who.int/mediacentre/factsheets/fs297/en/
, last visited January 2018.
2. Gatuha, G. and Jiang, T., Evaluating diagnostic performance of machine learning algorithms on breast cancer. In International Conference on Intelligent Science and Big Data Engineering. Springer, 2015.
3. Witten, I. H., et al., Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2016.
4. Mohanty, A. K., Senapati, M. R., and Lenka, S. K., RETRACTED ARTICLE: An improved data mining technique for classification and detection of breast cancer from mammograms. Neural Computing and Applications 22(1):303–310, 2013.
5. Xie, W., Li, Y., and Ma, Y., Breast mass classification in digital mammography based on extreme learning machine. Neurocomputing 173:930–941, 2016.
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