Texture Analysis in the Evaluation of COVID-19 Pneumonia in Chest X-Ray Images: A Proof of Concept Study

Abstract

Background: One of the most challenging aspects related to Covid-19 is to establish the presence of infection in an early phase of the disease. Texture analysis might be an additional tool for the evaluation of Chest X-ray in patients with clinical suspicion of Covid-19 related pneumo-nia. Objective: To evaluate the diagnostic performance of texture analysis and machine learning models for the diagnosis of Covid-19 interstitial pneumonia in Chest X-ray images. Methods: Chest X-ray images were accessed from a publicly available repository(https://www.kag-gle.com/tawsifurrahman/covid19-radiography-database). Lung areas were manually segmented us-ing a polygonal region of interest covering both lung areas, using MaZda, a freely available soft-ware for texture analysis. A total of 308 features per ROI was extracted. One hundred-ten Covid-19 Chest X-ray images were selected for the final analysis. Results: Six models, namely NB, GLM, DL, GBT, ANN, and PLS-DA were selected and ensem-bled. According to Youden's index, the Covid-19 Ensemble Machine Learning Score showing the highest area under the curve (0.971 +/- 0.015) was 132.57. Assuming this cut-off the Ensemble model performance was estimated by evaluating both true and false positive/negative, resulting in 91.8% accuracy with 93% sensitivity and 90% specificity. Moving the cut-off value to-100, although the accuracy resulted lower (90.6%), the Ensemble Machine Learning showed 100% sensitivity, with 80% specificity. Conclusion: Texture analysis of Chest X-ray images and machine learning algorithms may help in differentiating patients with Covid-19 pneumonia. Despite several limitations, this study can lay the ground for future research works in this field and help to develop more rapid and accurate screening tools for these patients.