Evaluating the performance of different classification methods on medical X-ray images

Abstract

X-ray images are broadly used for diagnosis and analysis in medical science. In a number of medical applications, such as disease detection, automatic classification systems are very beneficial. Therefore, finding an appropriate classification technique is helpful. In this paper, it is aimed to compare the performance of different classification methods on categorizing X-ray images. In this regard, we have done our experiments in two phases. In phase 1, the effect of wavelet transformation (WT) as a feature selection method is evaluated. Obtaining almost similar performance (about 78%) for probabilistic neural network (PNN) and WT-based PNN, while reducing computational cost, leads us to apply WT on datasets in phase 2. During phase 2, random forest (RF), decision tree (DT), support vector machine (SVM) and Naive Bayes (NB) are applied on obtained images from WT. The results reveal better performance of RF compared to other methods by 82% accuracy. With very close accuracy, SVM got the next place (81%). DT and NB classifiers are the next ones by about 66% accuracy.