Application of radial basis function neural network for grading of gliomas.
- Author:
Zhongxin ZHAO
1
;
Kai LAN
;
Peng XU
;
Yu ZHANG
;
Jiahe XIAO
;
Min HE
Author Information
1. Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu 610041, China.
- Publication Type:Journal Article
- MeSH:
Brain Neoplasms;
pathology;
Glioma;
pathology;
Humans;
Image Processing, Computer-Assisted;
Magnetic Resonance Imaging;
Neoplasm Grading;
Neural Networks (Computer)
- From:
Journal of Biomedical Engineering
2010;27(6):1384-1388
- CountryChina
- Language:Chinese
-
Abstract:
This retrospective investigation was directed to the applicability of Radial Basis Function Neural Network (RBF-NN) and Discriminant Analysis in the grading of gliomas. The data on 116 patients with primary glioma in our hospital from February 2008 to April 2009 were collected. Kruskal-Wallis H test was used to draw in the variable age ranks and then to take them out from the range of different grades of gliomas. The results of RBF-NN model, discriminant analysis, and the combined model of RBF-NN and discriminant analysis were evaluated and compared respectively with and without age. In this study, different classifications of gliomas showed statistically significant differences in age: and the accuracy of the models with age was better than the ones without age. The predictive accuracy and Kappa value of RBF-NN model and the combined model were also better than those exhibited by Bayes discriminant analysis. Consequently, as a prediction model, or to help other models, RBF-NN is of significance to predicting the grade of gliomas.
