A subset polynomial neural networks approach for breast cancer diagnosis

Terry J. O'Neill, Jack Penm, Jonathan Penm

Research output: Contribution to journalArticleResearchpeer-review

8 Citations (Scopus)

Abstract

Breast cancer is a very common and serious cancer for women that is diagnosed in one of every eight Australian women before the age of 85. The conventional method of breast cancer diagnosis is mammography. However, mammography has been reported to have poor diagnostic capability. In this paper we have used subset polynomial neural network techniques in conjunction with fine needle aspiration cytology to undertake this difficult task of predicting breast cancer. The successful findings indicate that adoption of NNs is likely to lead to increased survival of women with breast cancer, improved electronic healthcare, and enhanced quality of life.

Original languageEnglish
Pages (from-to)293-302
Number of pages10
JournalInternational Journal of Electronic Healthcare
Volume3
Issue number3
DOIs
Publication statusPublished - 14 Aug 2007
Externally publishedYes

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Breast Neoplasms
Mammography
Fine Needle Biopsy
Cell Biology
Quality of Life
Delivery of Health Care
Survival
Neoplasms

Cite this

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A subset polynomial neural networks approach for breast cancer diagnosis. / O'Neill, Terry J.; Penm, Jack; Penm, Jonathan.

In: International Journal of Electronic Healthcare, Vol. 3, No. 3, 14.08.2007, p. 293-302.

Research output: Contribution to journalArticleResearchpeer-review

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