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Lookup NU author(s): Dan Petrovici, Dr Madurai Lakshmi, Dr Gajanan Sherbet
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This paper presents a new hybrid system developed for nodal involvement assessment in breast cancer patients. The hybrid system integrates a neural network and fuzzy rule-based system learning methods. The data used in this study were collected from 100 women who were clinically diagnosed with breast cancer in the form of carcinoma or benign conditions. The data set contains seven different histological and cytological factors, and two nodal outputs (positive and negative nodal status) to be predicted for nodal involvement assessment in breast cancer patients. The hybrid system yielded the highest predictive accuracy of 73%, compared with statistical, neural networks and fuzzy logic methods. The overall results are encouraging and reveal the efficiency of the hybrid system.
Author(s): Seker H, Odetayo MO, Petrovic D, Naguib RNG, Bartoli C, Alasio L, Lakshmi MS, Sherbet GV
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Annual International Conference of the IEEE Engineering in Medicine and Biology
Year of Conference: 2002
Pages: 1049-1050
ISSN: 1094-687X
Publisher: IEEE
URL: http://dx.doi.org/10.1109/IEMBS.2002.1106270
DOI: 10.1109/IEMBS.2002.1106270
Library holdings: Search Newcastle University Library for this item
ISBN: 0780376129
