Comparative Performance Analysis of Machine Learning Classifiers in Detection of Childhood Pneumonia Using Chest Radiographs | Zendy

Rafael T. Sousa | Zendy; Oge Marques | Zendy; Fabrízzio Soares | Zendy; Iwens I.G. Sene | Zendy; Leandro L.G. de Oliveira | Zendy; Edmundo Sérgio Spoto | Zendy

Open Access

Comparative Performance Analysis of Machine Learning Classifiers in Detection of Childhood Pneumonia Using Chest Radiographs

Author(s) -

Rafael T. Sousa,

Oge Marques,

Fabrízzio Soares,

Iwens I.G. Sene,

Leandro L.G. de Oliveira,

Edmundo Sérgio Spoto

Publication year - 2013

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2013.05.444

Subject(s) - computer science , support vector machine , artificial intelligence , naive bayes classifier , robustness (evolution) , machine learning , classifier (uml) , pattern recognition (psychology) , bayes' theorem , radiography , k nearest neighbors algorithm , bayesian probability , radiology , medicine , biochemistry , chemistry , gene

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: Näıve 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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