Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study. Issue 128 (July 2020) - Wu, Xiangjun; Hui, Hui; Niu, Meng; Li, Liang; Wang, Li; He, Bingxi; Yang, Xin; Li, Li; Li, Hongjun; Tian, Jie; Zha, Yunfei
Highlights: Deep learning method enables rapid identification of COVID-19 on chest CT exams. Deep learning method mitigates the radiologists' workload in COVID-19 screening. It is essential to validate the deep learning method in large-scale dataset. Abstract: Purpose: To develop a deep learning-based method to assist radiologists to fast and accurately identify patients with COVID-19 by CT images. Methods: We retrospectively collected chest CT images of 495 patients from three hospitals in China. 495 datasets were randomly divided into 395 cases (80%, 294 of COVID-19, 101 of other pneumonia) of the training set, 50 cases (10%, 37 of COVID-19, 13 of other pneumonia) of the validation set and 50 cases (10%, 37 of COVID-19, 13 of other pneumonia) of the testing set. We trained a multi-view fusion model using deep learning network to screen patients with COVID-19 using CT images with the maximum lung regions in axial, coronal and sagittal views. The performance of the proposed model was evaluated by both the validation and testing sets. Results: The multi-view deep learning fusion model achieved the area under the receiver-operating characteristics curve (AUC) of 0.732, accuracy of 0.700, sensitivity of 0.730 and specificity of 0.615 in validation set. In the testing set, we can achieve AUC, accuracy, sensitivity and specificity of 0.819, 0.760, 0.811 and 0.615 respectively. Conclusions: Based on deep learning method, the proposed diagnosis model trained on multi-view images of chest CT images showed great potential to improve the efficacy of diagnosis and mitigate the heavy workload of radiologists for the initial screening of COVID-19 pneumonia.