Deeping Learning Can Aid in Identifying the Coronavirus Disease 2019 (COVID-19) from CT images: A Multi-center Study

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Abstract

     The coronavirus disease 2019 (COVID-19) is raging inside China and internationally. We develop a deep learning model to discriminate patients with COVID-19 from other pneumonias using routine CT images of 807 patients (six centers). Patient from three of the centers were randomly divided into a training cohort (n=346) and an internal validation cohort (n=152). Patients from the other three centers were enrolled as an external validation cohort (n=309). We built a three-dimensional convolutional neural network model (termed as COVID-19-3DCNN). The inputs were resized and cropped the lung regions to the size of (20, 243, 243). We trained the COVID-19-3DCNN on the training cohort using a NVIDIA Titan RTX Graphics Card for 60 epochs. The batch size was 64. We set a learning rate of 1e-5 and a weight-decay of 1. The binary cross entropy loss was adopted as the model’s criterion. The Adam optimizer was applied to step the model’s weights. Finally, the model achieves an impressive performance.