Evaluation of Classifiers to a Childhood Pneumonia Computer-Aided Diagnosis System

Abstract

This work extends PneumoCAD, a Computer-Aided Diagnosis system for detecting pneumonia in infants using radiographic images, with the aim of improving the system’s accuracy and robustness. We implement and compare five contemporary machine learning classifiers, namely: Naive Bayes, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Multi-Layer Perceptron (MLP) and Decision Tree, combined with three dimensionality reduction algorithms: Sequential Forward Selection (SFS), Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA). Current results demonstrate that Naive Bayes classifier combined with KPCA produces the best overall results.

Publication
In Proceedings of IEEE 27th International Symposium on Computer-Based Medical Systems (CBMS)
Fabrizzio Soares
Fabrizzio Soares
Associate Professor and CS Chair

Fabrizzio Soares is a professor of Computer Science, Information Systems and Software Engineering at INF/UFG. His research interests include Computer Vision, Human Computer Interaction, Machine Learning and Programming topics. He is the leader of the Pixellab group, which develops solutions for accesibilty, Precision Agriculture, and Interactive Systems.

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