Open AccessA Comparison of Radial Basis Function and Multilayer Perceptron Network as tool for classification of Medical Data
Author(s) -
S. Sharmila,
C. Dharuman,
P Venkatesan
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1377/1/012028
Subject(s) - radial basis function , artificial neural network , multilayer perceptron , computer science , artificial intelligence , perceptron , basis (linear algebra) , radial basis function network , machine learning , function (biology) , pattern recognition (psychology) , mathematics , geometry , evolutionary biology , biology
Artificial Neural Network has become a popular tool in developing systems that encircles human proficiency. The importance of exact detection is exceptionally important for proper treatment and preserve of disease. Clinical cytology has improved tremendously in disease diagnosis. In this paper, two Artificial Neural Network (ANN) methods, Multilayer Perceptron (MLP) and Radial Basis Function (RBF) network are compared. The RBF network predicts comparatively better accuracy in compared to MLP methods. Also it was detected that the RBF method requires a lesser amount of time for the development of the model, this is because there is no repetition to reach the favourable parameters in the model.