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. 2020 May 5;128:109041.
doi: 10.1016/j.ejrad.2020.109041. Online ahead of print.

Deep Learning-Based Multi-View Fusion Model for Screening 2019 Novel Coronavirus Pneumonia: A Multicentre Study

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Free PMC article

Deep Learning-Based Multi-View Fusion Model for Screening 2019 Novel Coronavirus Pneumonia: A Multicentre Study

Xiangjun Wu et al. Eur J Radiol. .
Free PMC article

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.

Keywords: Computed tomography; Coronavirus disease 2019; Deep learning; Multi-view model.

Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
The main framework of multi-view deep learning fusion model. We firstly extracted the lung region in CT slices using threshold segmentation method. Then, we trained our model based on the architecture of ResNet50. The inputs of the model are the corresponding CT images in axial, coronal, and sagittal views that selected from the maximum lung region selection. The three branch networks output feature maps that aggregated to feed into a fully connected dense layer. Finally, the layer outputs the risk value of COVID-19 pneumonia to evaluate the performance of the deep learning model.
Fig. 2
Fig. 2
ROC curves of single-view (a) and multi-view (b) deep learning diagnosis model of COVID-19 pneumonia. The confusion matrix of two diagnosis models (c). 1 represents COVID-19 pneumonia, and 0 represents other pneumonia.
Fig. 3
Fig. 3
ROC curve of the gender group of multi-view deep learning diagnosis model of COVID-19 pneumonia.
Fig. 4
Fig. 4
ROC curve of the age group of multi-view deep learning diagnosis model of COVID-19 pneumonia.
Fig. 5
Fig. 5
Representative examples of pneumonia diagnosis for a 46-year-old male with COVID-19 pneumonia (a), an 84-year-old female with bacterial pneumonia in validation set (b), a 62-year-old male with COVID-19 pneumonia (c) and a 52-year-old female with bacterial pneumonia in testing set (d). The risk scores of these four patients with COVID-19 infections are 0.801, 0.461, 0.946, and 0.315 (range from 0-1), respectively, which assessed by the multi-view deep learning fusion model. The cut-off of the model is 0.653. The ground glass opacity in COVID-19 patients are marked with red arrows (a, c).

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