Severity assessment of COVID-19 using imaging descriptors: A deep-learning transfer learning approach from non-COVID-19 pneumonia

Chest x-ray (CXR) provides valuable diagnostic information during treatment monitoring of COVID-19 pneumonia. In this preliminary study, we show that deep learning-based imaging descriptors have the potential to quantitatively assess the severity of the disease. In the first stage, a deep convolutional neural network (DCNN). GoogLeNet, was trained to perform patch-level classification of non-COVID-19 pneumonia and normal image patches from the ChestX-ray14 data set. A total of 246,753 patches were used to train the DCNN in a four-fold cross-validation. The trained DCNN generates a pixel-wise pneumonia severity map when deployed to a CXR image. Global descriptors based on the intensity of the severity map were extracted and the classification accuracy was evaluated using a random forest classifier. In the second stage, the DCNN was deployed to 202 COVID-19 positive CXRs. Global descriptors were extracted and fine-tuned to generate severity measures for COVID-19 pneumonia. These image-level global descriptors were mapped to radiologist's severity rating using logistic regression by 2-fold cross-validation. Classification accuracy was measured using the area under the receiver operating characteristic (ROC) curve (AUC). For classification of non-COVID-19 pneumonia from normal CXR, the patch-level AUC of the DCNN was 0.91 +/- 0.03 and the AUC of the image-level global descriptors was 0.93 +/- 0.04. The COVID-19 pneumonia regression model showed that the global descriptors had a correlation of 0.68 with the severity of the pneumonia in the CXR. Using radiologist's rating of 0 as negative and higher ratings as positive for COVID-19 pneumonia, the scores from the regression model achieved an average AUC of 0.76 for classification in the validation sets.

Automated summary

The study demonstrates the potential of deep learning in quantitatively assessing the severity of COVID-19 pneumonia, showing high accuracy in level classification and extraction of global descriptors.