Back to Search
Start Over
Fuzzy rules to predict degree of malignancy in brain glioma.
- Source :
- Medical & Biological Engineering & Computing; Mar2002, Vol. 40 Issue 2, p145-152, 8p
- Publication Year :
- 2002
-
Abstract
- The current pre-operative assessment of the degree of malignancy in brain glioma is based on magnetic resonance imaging (MRI) findings and clinical data. 280 cases were studied, of which 111 were high-grade malignancies and 169 were low-grade, so that regular and interpretable patterns of the relationships between glioma MRI features and the degree of malignancy could be acquired. However, as uncertainties in the data and missing values existed, a fuzzy rule extraction algorithm based on a fuzzy min-max neural network (FMMNN) was used. The performance of a multi-layer perceptron network (MLP) trained with the error back-propagation algorithm (BP), the decision tree algorithm ID3, nearest neighbour and the original fuzzy min-max neural network were also evaluated. The results showed that two fuzzy decision rules on only six features achieved an accuracy of 84.6% (89.9% for low-grade and 76.6% for high-grade cases). Investigations with the proposed algorithm revealed that age, mass effect, oedema, postcontrast enhancement, blood supply, calcification, haemorrhage and the signal intensity of the T1-weighted image were important diagnostic factors. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01400118
- Volume :
- 40
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Medical & Biological Engineering & Computing
- Publication Type :
- Academic Journal
- Accession number :
- 50077221
- Full Text :
- https://doi.org/10.1007/BF02348118