Image analysis identifies salient features of COVID-19
In clinical practice, the diagnostic decision of a clinician relies on the identification of the SAs in the medical images by radiologists. The statistical results show that the performance of the CNNCF for the identification of COVID-19 is as good as that of the experts. A comparison consisting of two parts was performed to evaluate the discriminatory ability of the CNNCF. In the first part, we used Grad-CAM, which is a non-intrusive method to extract the salient features in medical images, to create a heatmap of the CNNCF result. Figure 2b shows the heatmaps of four examples of COVID-19 cases in the X-data and CT-data. In the second part, we used density-based spatial clustering of applications with noise (DBSCAN) to calculate the center pixel coordinates (CPC) of the salient features corresponding to COVID-19. All CPCs were normalized to a range of 0 to 1. Subsequently, we used a significance test (ST)42 to analyze the relationship between the CPC of the CNNCF output and the CPC annotated by the experts. A good performance was obtained, with a mean square error (MSE) of 0.0108, a mean absolute error (MAE) of 0.0722, a root mean squared error (RMSE) of 0.1040, a correlation coefficient (r) of 0.9761, and a coefficient of determination (R2) of 0.8801.