The structure of the proposed framework, consisting of the stage I sub-framework and the stage II sub-framework is shown in Fig. 3a, where Q, L, M, and N are the hyper-parameters of the framework for general use cases. The values of Q, L, M, and N were 1, 1, 2, and 2, respectively, in this study; this framework referred to as the CNNCF framework. The stage I and stage II sub-frameworks were designed to extract features corresponding to different optimization goals in the analysis of the medical images. The performance of the CNNCF was evaluated using multi-modal data sets (X-data and CT-data) to ensure the generalization and transferability of the model, and five evaluation indicators were used (sensitivity, precision, specificity, F1, kappa). The salient features of the images extracted by the CNNCF were visualized in a heatmap (four examples are shown in Fig. 2b). In this study, multiple experiments were conducted (including experiments that included data from the same source and from different sources) to validate the generalization ability of the framework while avoiding the possible sample selection bias. Five experts evaluated the images, i.e., a 7th-year respiratory resident (Respira.), a 3rd-year emergency resident (Emerg.), a 1st-year respiratory intern (Intern), a 5th-year radiologist (Rad-5th), and a 3rd-year radiologist (Rad-3rd). The definition of the expert group can be found in Supplementary Note 1. The abbreviations of all the data sets used in the following experiments including XPDS, XPTS, XPVS, XHDS, XHTS, XHVS, CTPDS, CTPTS, CTPVS, CTHDS, CTHTS, CTHVS, CADS, CATS, CAVS, XMTS, XMVS, CTMTS, and CTMVS were defined in the “Methods” section (see “Data sets splitting” section). The following results were obtained.
Fig. 3 CNN-based frameworks.
a The classification framework for the identification of COVID-19. b The regression framework for the correlation analysis between the lesion areas and the clinical indicators. c is the workflow of the classification framework for the identification of COVID-19.