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.
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Tarantino, A.B.: 38. In: Nódulo Solitário Do Pulmão, 4th edn. Guanabara Koogan, Rio de Janeiro, pp. 733–753 (1997)
Erkel, A.R.V., Pattynama, P.M.T.: Receiver operating characteristic (ROC) analysis: Basic principles and applicattions in radiology. European Journal of Radiology 27, 88–94 (1998)
Clunie, D.A.: DICOM Structered Reporting. PixelMed Publishing, Pennsylvania (2000)
Nikolaidis, N., Pitas, I.: 3-D Image Processing Algorithms. John Wiley, New York (2001)
Silva, A.C., Carvalho, P.C.P.: Sistema de análise de nódulo pulmonar. In: II Workshop de Informática aplicada a Saúde, Itajai, Universidade de Itajai (2002), Available at http://www.cbcomp.univali.br/pdf/2002/wsp035.pdf
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Addison-Wesley, Reading (1992)
da Costa, L.F., Velte, T.J.: Automatic characterization and classification of glangion cells from the salamander retina. The Journal of Comparative Neurology 404, 33–51 (1999)
Zhou, Y., Toga, A.W.: Efficient skeletonization of volumetric objects. IEEE Transactions on Visualization and Computer Graphics 5, 196–208 (1999)
Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis and Machine Vision, 2nd edn. International Thomson Publishing (1998)
Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine Learning, Neural and Statistical Classification, NJ, USA. Ellis Horwood Series in Artificial Intelligence (1994)
Lachenbruch, P.A.: Discriminant Analysis. Hafner Press, New York (1975)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley-Interscience Publication, New York (1973)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1999)
Haykin, S.: Redes Neurais: Princípios e Prática, 2nd edn. Bookman, Porto Alegre (2001)
Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Kluwer Academic Publishers, Dordrecht (1998)
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, London (1990)
Lead Technologies: SPSS 11.0 for windows (2003), Available at http://www.spss.com
Chang, C.C., Lin, C.J.: LIBSVM – a library for support vector machines (2003), Available at http://www.csie.ntu.edu.tw/~cjlin/libsvm/
CPC-X Software: Neuralpower professional v. 1.0 (2003), Available at http://www.geocities.com/neuralpower/
Metz, C.E.: ROCKIT software (2003), Available at http://www-radiology.uchicago.edu/krl/toppage11.htm
Greinera, M., Pfeifferb, D., Smithc, R.: Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Preventive Veterinary Medicine 45, 23–41 (2000)
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Silva, A.C., de Paiva, A.C., de Oliveira, A.C.M. (2005). Comparison of FLDA, MLP and SVM in Diagnosis of Lung Nodule. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_28
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DOI: https://doi.org/10.1007/11510888_28
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