TELKOMNIKA Telecommunication, Computing, Electronics and Control
Construction of fuzzy radial basis function neural network model for diagnosing prostate cancer

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Construction of fuzzy radial basis function neural network model for diagnosing prostate cancer

Fuzzy C-means
Fuzzy RBFNN
Prostate cancer
Singular value decomposition

In this paper, we propose a construction of fuzzy radial basis function neural
network model for diagnosing prostate cancer. A fuzzy radial basis function
neural network (fuzzy RBFNN) is a hybrid model of logical fuzzy and neural
network. The fuzzy membership function of the fuzzy RBFNN model input
is developed using the triangle function. The fuzzy C-means method is
applied to estimate the center and the width parameters of the radial basis
function. The weight estimation is performed by various ways to gain the
most accurate model. A singular value decomposition (SVD) is exploited to
address this process. As a comparison, we perform other ways including back
propagation and global ridge regression. The study also promotes image
preprocessing using high frequency emphasis filter (HFEF) and histogram
equalization (HE) to enhance the quality of the prostate radiograph. The

features of the textural image are extracted using the gray level co-
occurrence matrix (GLCM) and gray level run length matrix (GLRLM). The

experiment results of fuzzy RBFNN are compared to those of RBFNN
model. Generally, the performances of fuzzy RBFNN surpass the RBFNN in
all accuracy calculation. In addition, the fuzzy RBFNN-SVD demonstrates
the most accurate model for prostate cancer diagnosis.

Agus Maman Abadi, Dhoriva Urwatul Wutsqa, Nurlia Ningsih

http://journal.uad.ac.id/index.php/TELKOMNIKA

Apr 9, 2021

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