Purpose: Coronavirus disease 2019 (COVID-19) is a new infection that has spread worldwide and with no automatic model to reliably detect its presence from images We aim to investigate the potential of deep transfer learning to predict COVID-19 infection using chest computed tomography (CT) and x-ray images Approach: Regions of interest (ROI) corresponding to ground-glass opacities (GGO), consolidations, and pleural effusions were labeled in 100 axial lung CT images from 60 COVID-19-infected subjects These segmented regions were then employed as an additional input to six deep convolutional neural network (CNN) architectures (AlexNet, DenseNet, GoogleNet, NASNet-Mobile, ResNet18, and DarkNet), pretrained on natural images, to differentiate between COVID-19 and normal CT images We also explored the model's ability to classify x-ray images as COVID-19, non-COVID-19 pneumonia, or normal Performance on test images was measured with global accuracy and area under the receiver operating characteristic curve (AUC) Results: When using raw CT images as input to the tested models, the highest accuracy of 82% and AUC of 88 16% is achieved Incorporating the three ROIs as an additional model inputs further boosts performance to an accuracy of 82 30% and an AUC of 90 10% (DarkNet) For x-ray images, we obtained an outstanding AUC of 97% for classifying COVID-19 versus normal versus other Combing chest CT and x-ray images, DarkNet architecture achieves the highest accuracy of 99 09% and AUC of 99 89% in classifying COVID-19 from non-COVID-19 Our results confirm the ability of deep CNNs with transfer learning to predict COVID-19 in both chest CT and x-ray images
Deep CNN models for predicting COVID-19 in CT and x-ray images