Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6868
Title: Implementing automated diagnostic systems for breast cancer detection
Authors: Übeyli, Elif Derya
Keywords: automated diagnostic systems
diagnostic accuracy
breast cancer detection
Issue Date: 2007
Publisher: Pergamon-Elsevier Science Ltd
Abstract: This paper intends to an integrated view of implementing automated diagnostic systems for breast cancer detection. The major objective of the paper is to be a guide for the readers, who want to develop an automated decision support system for detection of breast cancer. Because of the importance of making the right decision, better classification procedures for breast cancer have been searched. The classification accuracies of different classifiers, namely multilayer perceptron neural network (MLPNN), combined neural network (CNN), probabilistic neural network (PNN), recurrent neural network (RNN) and support vector machine (SVM), which were trained on the attributes of each record in the Wisconsin breast cancer database, were compared. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem. This research demonstrated that the SVM achieved diagnostic accuracies which were higher than that of the other automated diagnostic systems. (c) 2006 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.eswa.2006.08.005
https://hdl.handle.net/20.500.11851/6868
ISSN: 0957-4174
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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