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. 2022 Nov 21;13(2):572–584. doi: 10.21037/qims-22-531

Figure 2.

Figure 2

Illustration of our proposed COVID-19 classification method. Before training the classification network, images are preprocessed to improve data quality and augmented to increase the training dataset size. When a chest Radiograph image is fed into the deformable CNN, the network outputs a preliminary classification result and a 1,024-dimensional latent representation of the input image. Then, a LCAM is generated by lung region segmentation and the Grad-CAM++ method. A ROI mask is generated by thresholding the LCAM. Radiomics features extracted from the ROI are concatenated with the latent representation and then fed into different machine learning classifiers for classification. #, predicted class. CNN, convolutional neural network; COVID-19, coronavirus disease 2019; LCAM, lung region class activation map; ROI, region of interest.