Five statistical indices, including sensitivity, specificity, precision, kappa coefficient, and F1 were used to evaluate the performance of the CNNCF. The sensitivity is related to the positive detection rate and is of great significance in the diagnostic testing of COVID-19. The specificity refers to the ability of the model to correctly identify patients with the disease. The precision indicates the ability of the model to provide a positive prediction. The kappa demonstrates the stability of the model’s prediction. The F1 is the harmonic mean of precision and sensitivity. Good performance was achieved by the CNNCF based on the five statistical indices for the multi-modal image data sets (X-data and CT-data). The consistency between the model results and the expert evaluation was determined using McNemar’s test. The good performance demonstrated the model’s capacity of learning from the experts using the labels of the image data and mimicking the experts in diagnostic decision-making. The ROC and PRC of the CNNCF were used to evaluate the performance of the classification model50. The ROC is a probability curve that shows the trade-off between the true positive rate (TPR) and false-positive rate (FPR) using different threshold settings. The AUROC provides a measure of separability and demonstrated the discriminative capacity of the classification model. The larger the AUROC, the better the performance of the model is for predicting the true positive (TP) and true negative (TN) cases. The PRC shows the trade-off between the TPR and the positive predictive value (PPV) using different threshold settings. The larger the AUPRC, the higher the capacity of the model is to predict the TP cases. In our experiments, the CNNCF achieved high scores for both the AUPRC and AUROC (>99%) for the X-data and CT-data.