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Classification of Breast Masses in Mammograms Using Radial Basis Functions and Simulated Annealing
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Author(s): Rafael do Espírito Santo (Universidade de São Paulo, Universidade Nove de Julho, and Instituto Israelita de Pesquisa e Ensino Albert Einstein, Brazil), Roseli de Deus Lopes (Universidade de São Paulo, Brazil)and Rangaraj M. Rangayyan (University of Calgary, Canada)
Copyright: 2011
Pages: 11
Source title:
Transdisciplinary Advancements in Cognitive Mechanisms and Human Information Processing
Source Author(s)/Editor(s): Yingxu Wang (Univeristy of Calgary, Canada)
DOI: 10.4018/978-1-60960-553-7.ch014
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Abstract
We present pattern classification methods based upon nonlinear and combinational optimization techniques, specifically, radial basis functions (RBF) and simulated annealing (SA), to classify masses in mammograms as malignant or benign. Combinational optimization is used to pre-estimate RBF parameters, namely, the centers and spread matrix. The classifier was trained and tested, using the leave-one-out procedure, with shape, texture, and edge-sharpness measures extracted from 57 regions of interest (20 related to malignant tumors and 37 related to benign masses) manually delineated on mammograms by a radiologist. The classifier’s performance, with pre-estimation of the parameters, was evaluated in terms of the area Az under the receiver operating characteristics curve. Values up to Az = 0.9997 were obtained with RBF-SA with pre-estimation of the centers and spread matrix, which are better than the results obtained with pre-estimation of only the RBF centers, which were up to 0.9470. Overall, the results with the RBF-SA method were better than those provided by standard multilayer perceptron neural networks
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