Texture-based Feature Extraction for COVID-19 Pneumonia Classification using Chest Radiography

Abstract

INTRODUCTION: The identification of COVID-19 pneumonia using chest radiography is challenging. OBJECTIVES: We investigate classification models to differentiate COVID-19-based and typical pneumonia in chest radiography. METHODS: We use 136 segmented chest X-rays to train and evaluate the performance of support vector machine (SVM), random forest (RF), AdaBoost (AB), and logistic regression (LR) classification methods. We use the PyRadiomics to extract statistical texture-based features in the right, left, and in six lung zones. We use a stratified k-folds (k=5) cross-validation within the training dataset, selecting the most relevant features with validation accuracy and relative feature importance. RESULTS: The AB model seems to be the best discriminant method, using six lung zones (AUC = 0.98). CONCLUSION: Our study shows a predominance of radiomic texture-based features related to COVID-19 pneumonia within the right lung, with a tendency within the upper lung zone.