Applying feature selection for effective classification of microcalcification clusters in mammograms

Dong Wang, Jinchang Ren, Jianmin Jiang , Stan S. Ipson

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

5 Citations (Scopus)

Abstract

Classification of benign and malignant microcalcification clusters (MCC) in mammograms plays an essential role for early detection of breast cancer in computer aided diagnosis (CAD) systems, where feature selection is desirable to improve both the efficiency and robustness of the classifiers. In this paper, three approaches are applied for this task, including feature selection using a neural classifier, a clustering criterion and a combined scheme. To evaluate the performance of these feature selection approaches, a same neural classifier is then applied using the selected features and the classification results are then compared. In our dataset in total 748 MCC samples are detained from the
well-known DDSM database, where 39 features are extracted for each sample. Comprehensive experiments with quantitative evaluations have demonstrated that the best classification rate can be achieved using 15-20 selected features. Also it is found that applying features selected from clustering rules can yield better performance in separate and combined scheme.
Original languageEnglish
Title of host publicationProceedings for 10th International Conference for Computer and Information Technology (CIT)
PublisherIEEE
Pages1384-1387
Number of pages4
ISBN (Print)9781424475476
DOIs
Publication statusPublished - 2010
EventComputer and Information Technology (CIT), 2010 IEEE 10th International Conference on - Bradford, United Kingdom
Duration: 29 Jun 20101 Jul 2010

Conference

ConferenceComputer and Information Technology (CIT), 2010 IEEE 10th International Conference on
CountryUnited Kingdom
CityBradford
Period29/06/101/07/10

Keywords

  • artificial neural networks
  • mammography
  • indexes
  • feature extraction
  • breast cancer
  • microcalcification

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  • Cite this

    Wang, D., Ren, J., Jiang , J., & Ipson, S. S. (2010). Applying feature selection for effective classification of microcalcification clusters in mammograms. In Proceedings for 10th International Conference for Computer and Information Technology (CIT) (pp. 1384-1387). IEEE. https://doi.org/10.1109/CIT.2010.247