Fuzzy-neural networks for medical diagnosis
No Thumbnail Available
Date
2010
Authors
Şenol, Canan
Yıldırım, Tülay
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
In this paper a novel fuzzy-neural network architecture is proposed and the algorithm is developed. Using this new architecture fuzzy-CSFNN fuzzy-MLP and fuzzy-RBF configurations were constituted and their performances have been compared on medical diagnosis problems. Here conic section function neural network (CSFNN) is also a hybrid neural network structure that unifies the propagation rules of multilayer perceptron (MLP) and radial basis function (RBF) neural networks at a unique network by its distinctive propagation rules. That means CSFNNs accommodate MLPs and RBFs in its own self-network structure. The proposed hybrid fuzzy-neural networks were implemented in a well-known benchmark medical problems with real clinical data for thyroid disorders breast cancer and diabetes disease diagnosis. Simulation results show that proposed hybrid structures outperform both MATLAB-ANFIS and non-hybrid structures. © 2010 Inderscience Enterprises Ltd.
Description
Keywords
fuzzy neural networks, fuzzy-CSFNN, fuzzy-MLP, fuzzy-neural hybrid schemes, fuzzy-RBF, medical diagnosis
Turkish CoHE Thesis Center URL
Citation
1
WoS Q
N/A
Scopus Q
Q4
Source
Volume
2
Issue
Start Page
265
End Page
271