CovXR: Automated Detection of COVID-19 Pneumonia in Chest X-Rays through Machine Learning
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Coronavirus disease 2019 (COVID-19) is the highly contagious illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The standard diagnostic testing procedure for COVID-19 is testing a nasopharyngeal swab for SARS-CoV-2 nucleic acid using a real-time polymerase chain reaction (PCR), which can take multiple days to provide a diagnosis. Another widespread form of testing 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. Patients who test positive for COVID-19 demonstrate diffuse alveolar damage in 87 cases. Machine learning has proven to have advantages in image classification problems with radiology. In this work, we introduce CovXR as a machine learning model designed to detect COVID-19 pneumonia in chest X-rays (CXR). CovXR is a convolutional neural network (CNN) trained on over 4,300 chest X-rays. The performance of the model is measured through accuracy, F1 score, sensitivity, and specificity. The model achieves an accuracy of 95.5 0.954. The sensitivity is 93.5 95 pneumonia on CXRs. The model achieves better accuracy than prior work and uses a unique approach to identify COVID-19 pneumonia. CovXR is highly accurate in identifying COVID-19 on CXRs of patients with a PCR confirmed positive diagnosis and provides much faster results than PCR tests.
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