Radial Basis Function Neural Network for Classification of Quantitative EEG in Patients with Advanced Chronic Renal Failure

2011 ◽  
pp. 411-418 ◽  
Author(s):  
Juan A. Barios ◽  
César Gonzalez ◽  
Bettina Benbunan ◽  
Victor Fernández-Armayor ◽  
José L. Teruel ◽  
...  
Keyword(s):  
Neural Network ◽  
Renal Failure ◽  
Chronic Renal Failure ◽  
Radial Basis Function ◽  
Basis Function ◽  
Quantitative Eeg ◽  
Radial Basis

scholarly journals Lung Cancer Classification Employing Proposed Real Coded Genetic Algorithm Based Radial Basis Function Neural Network Classifier

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
I. Jasmine Selvakumari Jeya ◽  
S. N. Deepa
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Minimum Mean Square Error ◽  
Gaussian Kernel ◽  
Neural Network Classifier ◽  
Real Coded Genetic Algorithm ◽  
Radial Basis

A proposed real coded genetic algorithm based radial basis function neural network classifier is employed to perform effective classification of healthy and cancer affected lung images. Real Coded Genetic Algorithm (RCGA) is proposed to overcome the Hamming Cliff problem encountered with the Binary Coded Genetic Algorithm (BCGA). Radial Basis Function Neural Network (RBFNN) classifier is chosen as a classifier model because of its Gaussian Kernel function and its effective learning process to avoid local and global minima problem and enable faster convergence. This paper specifically focused on tuning the weights and bias of RBFNN classifier employing the proposed RCGA. The operators used in RCGA enable the algorithm flow to compute weights and bias value so that minimum Mean Square Error (MSE) is obtained. With both the lung healthy and cancer images from Lung Image Database Consortium (LIDC) database and Real time database, it is noted that the proposed RCGA based RBFNN classifier has performed effective classification of the healthy lung tissues and that of the cancer affected lung nodules. The classification accuracy computed using the proposed approach is noted to be higher in comparison with that of the classifiers proposed earlier in the literatures.

A Radial Basis Function Neural Network (RBFNN) Approach for Structural Classification of Thyroid Diseases

2008 ◽  
Vol 32 (3) ◽  
pp. 215-220 ◽  
Author(s):  
Rızvan Erol ◽  
Seyfettin Noyan Oğulata ◽  
Cenk Şahin ◽  
Z. Nazan Alparslan
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Thyroid Diseases ◽  
Structural Classification ◽  
Radial Basis

Classification of Mammogram Images Using Radial Basis Function Neural Network

2019 ◽  
pp. 311-320
Author(s):  
Ashraf Osman Ibrahim ◽  
Ali Ahmed ◽  
Aleya Abdu ◽  
Rahma Abd-alaziz ◽  
Mohamed Alhaj Alobeed ◽  
...  
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis ◽  
Mammogram Images

Classification of microcalcifications in digital mammograms using trend-oriented radial basis function neural network

1999 ◽  
Vol 32 (5) ◽  
pp. 891-903 ◽  
Author(s):  
Osamu Tsujii ◽  
Matthew T. Freedman ◽  
Seong K. Mun
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis
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