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PNN/GRNN ensemble processor design for early screening of breast cancer

  • Walker H. Land
  • , Xinpei Ma
  • , Erin Barnes
  • , Xingye Qiao
  • , John Heine
  • , Timothy Masters
  • , Jin Woo Park
  • State University of New York Binghamton University
  • Moffitt Cancer Center
  • TMAIC

Research output: Contribution to journalConference articlepeer-review

11 Scopus citations

Abstract

Breast cancer screening has reference to screening of asymptomatic, generally healthy women for breast cancer, to identify those who should receive a follow up check. Early screening can detect non-invasive ductal carcinoma in situ (called "pre breast cancer"), which almost never forms a lump and is generally non-detectible, except by mammography. This paper will describe the design and preliminary evaluation of this PNN/GRNN ensemble pre-screener, in the context of a possible pre-screening protocol, which may, if required, include other data. The results show that using the ensemble technique provides almost a 20% AUC increase over the average standalone PNN and almost 10% over the best performing PNN.

Original languageEnglish
Pages (from-to)438-443
Number of pages6
JournalProcedia Computer Science
Volume12
DOIs
StatePublished - 2012
Event2012 Complex Adaptive Systems Conference - Washington, DC, United States
Duration: Nov 14 2012Nov 16 2012

Keywords

  • Bioinformatics
  • Biomedical
  • Breast cancer screening
  • Statistical neural networks

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