Comparative Evaluation of Feature Extraction Techniques in Chest X Ray Image with Different Classification Model

Artificial intelligence (AI) has the potential to transform health care as it has revolutionized many pattern recognition applications. During the last few years, medical image analysis has been gaining attention. Research on medical images using machine learning (ML) has made significant progress. The purpose of this study was to compare the accuracy of classification in clinical images among ML algorithms. Based on features extracted technique local binary patterns (LBP), histograms of gradients (HOG) and pixels feature extractor, seven classification models are compared. Several methods are used to classify important features are obtained by different feature extractors, including support vector machines (SVM), decision trees (DT), logistic regression(LR), random forests (RF), extreme gradient boosting (XGB), K-Neighbors classifiers (KN) and multinomial Naive Bayes (NB). To test the accuracy of our classification and feature extraction models specifically for histopathology images, we used COVID-19 chest radiographs, which is available publicly dataset containing 21,212 CT images divided into four classes. In comparison to other feature extractors, SVM has the better result using HOG as features. LPB feature extraction has been shown to be superior when used with SVM algorithm to classify COVID-19 chest radiograph data, as demonstrated by experiments on COVID-19 chest radiograph data.