Comparative performance analysis of machine learning classifiers in detection of childhood pneumonia using chest radiographs

被引：31

作者：

Sousa, Rafael T. ^{[1
]}

Marques, Oge ^{[2
]}

Soares, Fabrizzio Alphonsus A. M. N. ^{[1
]}

Sene, Iwens I. G., Jr. ^{[1
]}

de Oliveira, Leandro L. G. ^{[1
]}

Spoto, Edmundo S. ^{[1
]}

机构：

[1] Univ Fed Goias, Inst Informat, Caixa Postal 131, BR-74001970 Goiania, Go, Brazil

[2] Florida Atlantic Univ, Dept Comp & Elect Engn,Comp Sci, Boca Raton, FL 33431 USA

来源：

2013 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE | 2013年 / 18卷

关键词：

Machine Learning; Computer-Aided Diagnosis (CAD); Childhood pneumonia; COMPUTER-AIDED DIAGNOSIS;

D O I：

10.1016/j.procs.2013.05.444

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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: Naive Bayes, K-Nearest Neighbor (KNN), and Support Vector Machines (SVM). Results of our experiments demonstrate that the SVM classifier produces the best overall results.

引用

页码：2579 / 2582

页数：4

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