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?:abstract
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Portable chest X-ray (pCXR) has become an indispensable tool in the management of Coronavirus Disease 2019 (COVID-19) lung infection This study employed deep-learning convolutional neural networks to classify COVID-19 lung infections on pCXR from normal and related lung infections to potentially enable more timely and accurate diagnosis This retrospect study employed deep-learning convolutional neural network (CNN) with transfer learning to classify based on pCXRs COVID-19 pneumonia (N = 455) on pCXR from normal (N = 532), bacterial pneumonia (N = 492), and non-COVID viral pneumonia (N = 552) The data was randomly split into 75% training and 25% testing, randomly A five-fold cross-validation was used for the testing set separately Performance was evaluated using receiver-operating curve analysis Comparison was made with CNN operated on the whole pCXR and segmented lungs CNN accurately classified COVID-19 pCXR from those of normal, bacterial pneumonia, and non-COVID-19 viral pneumonia patients in a multiclass model The overall sensitivity, specificity, accuracy, and AUC were 0 79, 0 93, and 0 79, 0 85 respectively (whole pCXR), and were 0 91, 0 93, 0 88, and 0 89 (CXR of segmented lung) The performance was generally better using segmented lungs Heatmaps showed that CNN accurately localized areas of hazy appearance, ground glass opacity and/or consolidation on the pCXR Deep-learning convolutional neural network with transfer learning accurately classifies COVID-19 on portable chest X-ray against normal, bacterial pneumonia or non-COVID viral pneumonia This approach has the potential to help radiologists and frontline physicians by providing more timely and accurate diagnosis
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