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Deep convolutional neural network (DCNN) architecture for the machine diagnosis of NCIPs. The training of this DCNN is a two-stage processes. In the first stage, we train the DCNN to distinguish the region of interests (ROIs) with the likelihood of NCIP into two classes: NCIPs or otherwise. In the second stage, we fine-tune the DCNN to predict the case-based probability of COVID-19 by ensembling the most significant location proposals self-produced in the DCNN.
