Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

Manual investigation of chest radiography (CXR) images by physicians is crucial for effective decision-making in COVID-19 diagnosis. However, the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques. This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies, including normal cases. Texture information is extracted using gray co-occurrence matrix (GLCM)-based features, while vessel-like features are obtained using Frangi, Sato, and Meijering filters. Machine learning models employing Decision Tree (DT) and Random Forest (RF) approaches are designed to categorize CXR images into common lung infections, lung opacity (LO), COVID-19, and viral pneumonia (VP). The results demonstrate that the fusion of texture and vessel-based features provides an effective ML model for aiding diagnosis. The ML model validation using performance measures, including an accuracy of approximately 91.8% with an RF-based classifier, supports the usefulness of the feature set and classifier model in categorizing the four different pathologies. Furthermore, the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogram -based analysis. This analysis reveals varying natural pixel distributions in CXR images belonging to the normal, COVID-19, LO, and VP groups, motivating the incorporation of additional features such as mean, standard deviation, skewness, and percentile based on the filtered images. Notably, the study achieves a considerable improvement in categorizing COVID-19 from LO, with a true positive rate of 97%, further substantiating the effectiveness of the methodology implemented.