  Early detection of the coronavirus disease 2019 (COVID-19) helps to treat
patients timely and increase the cure rate, thus further suppressing the spread
of the disease. In this study, we propose a novel deep learning based detection
and similar case recommendation network to help control the epidemic. Our
proposed network contains two stages: the first one is a lung region
segmentation step and is used to exclude irrelevant factors, and the second is
a detection and recommendation stage. Under this framework, in the second
stage, we develop a dual-children network (DuCN) based on a pre-trained
ResNet-18 to simultaneously realize the disease diagnosis and similar case
recommendation. Besides, we employ triplet loss and intrapulmonary distance
maps to assist the detection, which helps incorporate tiny differences between
two images and is conducive to improving the diagnostic accuracy. For each
confirmed COVID-19 case, we give similar cases to provide radiologists with
diagnosis and treatment references. We conduct experiments on a large publicly
available dataset (CC-CCII) and compare the proposed model with
state-of-the-art COVID-19 detection methods. The results show that our proposed
model achieves a promising clinical performance.
