NDLI: Suitable MLP Network Activation Functions for Breast Cancer and Thyroid Disease Detection

Content Provider	IEEE Xplore Digital Library
Author	Isa, I.S. Saad, Z. Omar, S. Osman, M.K. Ahmad, K.A. Sakim, H.A.M.
Copyright Year	2010
Abstract	This paper compared various MLP activation functions for classification problems. The most well-known (Artificial Neural Network) ANN architecture is the Multilayer Perceptron (MLP) network which is widely used for solving problems related to data classifications. Selection of the activation functions in the MLP network plays an essential role on the network performance. A lot of studies have been conducted by reseachers to investigate special activation function to solve different kind of problems. Therefore, this paper intends to investigate the activation functions in MLP networks in terms of the accuracy performances. The activation functions under investigation are sigmoid, hyperbolic tangent, neuronal, logarithmic, sinusoidal and exponential. Medical diagnosis data from two case studies, thyroid disease classification and breast cancer classification, have been used to test the performance of the MLP network. The MLP networks are trained using Back Propagation learning algorithm. The performance of the MLP networks are calculated based on the percentage of correct classificition. The results show that the hyperbolic tangent function in MLP network had the capability to produce the highest accuracy for classifying breast cancer data. Meanwhile, for thyroid disease classification, neuronal function is the most suitable function that performed the highest accuracy in MLP network.
Starting Page	39
Ending Page	44
File Size	541837
Page Count	6
File Format	PDF
ISBN	9781424486526
DOI	10.1109/CIMSiM.2010.93
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-28
Publisher Place	Indonesia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Data classification Accuracy Neural network applications Activation function Multilayer perceptron network Artificial neural networks Breast cancer Classification algorithms Testing Diseases
Content Type	Text
Resource Type	Article

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