Comparative performance analysis of machine learning classifiers in detection of childhood pneumonia using chest radiographs
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Data
2013
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Resumo
This work extends PneumoCAD, a Computer-Aided Diagnosis system for detecting pneumonia in infants using radiographic
images [1], with the aim of improving the system’s accuracy and robustness. We implement and compare three contemporary
machine learning classifiers, namely: Na¨ıve Bayes, K-Nearest Neighbor (KNN), and Support Vector Machines (SVM). Results
of our experiments demonstrate that the SVM classifier produces the best overall results.
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Machine Learning, Computer-aided diagnosis (CAD), Childhood pneumonia
Citação
SOUSA, Rafael T.; MARQUES, Oge; SOARES, Fabrizzio Alphonsus A. M. N.; SENE JÚNIOR, Iwens I. G.; OLIVEIRA, Leandro L. G.; SPOTO, Edmundo S. Comparative performance analysis of machine learning classifiers in detection of childhood pneumonia using chest radiographs. Procedia Computer Science, New York, v. 18, n. 2013, p. 2579-2582, 2013.