Deep Convolutional Neural Networks for Accurate Diagnosis of COVID-19 Patients Using Chest X-Ray Image Databases from Italy, Canada, and the USA

Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), famously known as COVID-19, has quickly become a global pandemic. Chest X-ray (CXR) imaging has proven reliable, fast, and cost-effective for identifying COVID-19 infections, which presents with atypical unilateral patchy infiltration in the lungs like typical pneumonia. We employed the deep convolutional neural network (DCNN) ResNet-34 to detect and classify CXR images from patients with COVID-19, other viral pneumonias, and normal controls. Methods: We created a single database, containing 781 source CXR images for COVID-19 (n=240), other viral pneumonias (n=274), and normal controls (n=267) from four different international sub-databases: the Società Italiana di Radiologia Medica e Interventistica (SIRM), the GitHub Database, the Radiology Society of North America (RSNA), and the Kaggle Chest X-Ray Database. Images were resized, normalized without any augmentation, and arranged in m batches of 16 images before supervised training, testing, and cross-validation of the DCNN classifier. Results: The ResNet-34 had a diagnostic accuracy as of the receiver operating characteristic (ROC) curves of the truepositive rate versus the false-positive rate with the area under the curve (AUC) of 1.00, 0.99, and 0.99, for COVID-19, other viral pneumonia, and normal control CXR images, respectively. This accuracy implied identical high sensitivity and specificity values of 100%, 99%, and 99% for the three groups, respectively. ResNet-34 achieved identical sensitivity and specificity of 100%, 99.6%, and 98.9% for classifying CXR images of the three groups, with an overall accuracy of 99.5% for the testing subset for diagnosis/prognosis. Conclusion: Based on this high classification precision, we believe that the output activation map of the final layer of the ResNet-34 is a powerful tool for the accurate diagnosis of COVID-19 infection from CXR images.