Comparison of FLDA, MLP and SVM in diagnosis of lung nodule

Authors:
Aristófanes Corrêa Silva;Anselmo Cardoso de Paiva;Alexandre Cesar Muniz de Oliveira
Affiliations:
Department of Electrical Engineering, Federal University of Maranhão – UFMA, São Luís, MA, Brazil;Department of Computer Science, Federal University of Maranhão – UFMA, São Luís, MA, Brazil;Department of Computer Science, Federal University of Maranhão – UFMA, São Luís, MA, Brazil
Venue:
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2005

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Abstract

The purpose of the present work is to compare three classifiers: Fisher's Linear Discriminant Analysis, Multilayer Perceptron and Support Vector Machine to diagnosis of lung nodule. These algorithms are tested on a database with 36 nodules, being 29 benigns and 7 malignants. Results show that the three algorithms had similar performance on this particular task.