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

摘要

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 seventy 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.