Effects of different classifiers in detecting infectious regions in chest radiographs

This paper presents the effects of different types of classifiers when analysing the normal and infectious regions in chest radiographs. Three types of classifiers are experimented on: Rule-based, Bayesian and k-nearest neighbour´s. The evaluation is based on a few criteria, namely, the classification accuracy, misclassification (error), speed, Kappa statistic, ROC area, and other performance measures specifically the true and false positive rates, and precision and recall. The dataset consists of image features from a total of 102 chest radiographs. The normal and infectious lung regions are extracted and divided into non-overlapping sub-blocks prior to the image feature computation. The quantitative results are presented and discussed for consideration in further analysis of infectious lungs.