A strong correlation was observed between the lesion areas detected by the proposed framework and the clinical indicators
In clinical practice, multiple clinical indicators are analyzed to determine whether further examinations (i.e., medical image examination) are needed. These indicators can be used to assess the predictive ability of the model. In addition, various examinations are required to perform an accurate diagnosis in clinical practice. However, the correlations between the results of various examinations are often not clear. We used the stage II sub-framework and the regressor block of the CNNRF to conduct a correlation analysis between the lesion areas detected by the framework and five clinical indicators (white blood cell count, neutrophil percentage, lymphocyte percentage, procalcitonin, C-reactive protein) of COVID-19 using the CADS. The inputs of the CNNRF were the lesion area images of each case, and the output was a 5-dimensional vector describing the correlation between the lesion areas and the five clinical indicators.
The MAE, MSE, RMSE, r, and R2 were used to evaluate the results. The ST and the Pearson correlation coefficient (PCC)43 were used to determine the correlation between the lesion areas and the clinical indicators. A strong correlation was obtained, with MSE = 0.0163, MAE = 0.0941, RMSE = 0.1172, r = 0.8274, and R2 = 0.6465. At a significance level of 0.001, the value of r was 1.27 times the critical value of 0.6524. This result indicates a high and significant correlation between the lesion areas and the clinical indicators. The PCC was 0.8274 (range of 0.8–1.0), indicating a strong correlation. The CNNRF was trained on the CATS and evaluated using the CAVS. The initial learning rate was 0.01, and the optimization function was the stochastic gradient descent (SGD) method44. The parameters of the CNNRF were initialized using the Xavier initialization method45.