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


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.

In Proceedings of IEEE 27th International Symposium on Computer-Based Medical Systems (CBMS)