Background: Computed tomography (CT) is the preferred imaging method for evaluating lung infection in new coronavirus infection caused pneumonia. Our research aimed to construct a system based on deep learning for detecting 2019-nCoV pneumonia on high resolution CT, and relieve the working pressure of radiologists and potentially improve the efficiency of diagnosis. Methods: 21,661 CT scan images from 40 2019-nCoV pneumonia patients and 5,100 CT scan images from 24 non-2019-nCoV infected patients were collected to train convolutional neural network model to detect 2019-nCoV pneumonia. Eleven and thirty-one patients with and without 2019-nCoV pneumonia were collected for retrospectively testing. Twenty-seven consecutive patients undergoing CT scans in Feb, 5, 2020 in Renmin Hospital of Wuhan University were prospectively collected to evaluate and compare the efficiency of radiologists against 2019-CoV pneumonia with that of the model. Findings: The model achieved a per-patient sensitivity of 100%, specificity of 93.55%, accuracy of 95.24%, PPV of 84.62%, and NPV of 100%; a per-image sensitivity of 94.34%, specificity of 99.16%, accuracy of 98.85%, PPV of 88.37%, and NPV of 99.61% in retrospective dataset. For 27 prospective patients, the model achieved a comparable performance to that of expert radiologist with much shorter reading time (41.34s [IQR 39.76-44.48] vs. 115.50s [IQR 85.69-118.17] per patient). Interpretation: We have developed a deep learning model showing a comparable performance with expert radiologist using much shorter time. It holds great potential to improve the efficiency of diagnosis and relieve the pressure of radiologists in clinical practice.