A hybrid system for nodal involvement assessment in breast cancer patients

Presents a new hybrid system which 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...

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Bibliographic Details
Main Authors: Seker, H., Odetayo, M.O., Petrovic, D., Naguib, R.N.G., Bartoli, C., Alasio, L., Lakshmi, M.S., Sherbet, G.V.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Biomedical imaging
Breast cancer
Clustering algorithms
Fuzzy logic
Fuzzy neural networks
Fuzzy systems
Knowledge based systems
Medical diagnostic imaging
Neural networks
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Description
Summary:Presents a new hybrid system which 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.
ISSN:1094-687X
1558-4615
DOI:10.1109/IEMBS.2002.1106270