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Coronavirus disease 2019 (COVID-19) is the highly contagious illness causedby severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The standarddiagnostic testing procedure for COVID-19 is testing a nasopharyngeal swab forSARS-CoV-2 nucleic acid using a real-time polymerase chain reaction (PCR),which can take multiple days to provide a diagnosis. Another widespread form oftesting is rapid antigen testing, which has a low sensitivity compared to PCR,but is favored for its quick diagnosis time of usually 15-30 minutes. Patientswho test positive for COVID-19 demonstrate diffuse alveolar damage in 87% ofcases. Machine learning has proven to have advantages in image classificationproblems with radiology. In this work, we introduce CovXR as a machine learningmodel designed to detect COVID-19 pneumonia in chest X-rays (CXR). CovXR is aconvolutional neural network (CNN) trained on over 4,300 chest X-rays. Theperformance of the model is measured through accuracy, F1 score, sensitivity,and specificity. The model achieves an accuracy of 95.5% and an F1 score of0.954. The sensitivity is 93.5% and specificity is 97.5%. With accuracy above95% and F1 score above 0.95, CovXR is highly accurate in predicting COVID-19pneumonia on CXRs. The model achieves better accuracy than prior work and usesa unique approach to identify COVID-19 pneumonia. CovXR is highly accurate inidentifying COVID-19 on CXRs of patients with a PCR confirmed positivediagnosis and provides much faster results than PCR tests.

CovXR: Automated Detection of COVID-19 Pneumonia in Chest X-Rays through Machine Learning