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. 2022 Mar 21;8(2):869-890.
doi: 10.3390/tomography8020071.

EDNC: Ensemble Deep Neural Network for COVID-19 Recognition

Lin Yang  1 , Shui-Hua Wang  1 , Yu-Dong Zhang  1
Affiliations

EDNC: Ensemble Deep Neural Network for COVID-19 Recognition

Lin Yang et al. Tomography. .

Abstract

The automatic recognition of COVID-19 diseases is critical in the present pandemic since it relieves healthcare staff of the burden of screening for infection with COVID-19. Previous studies have proven that deep learning algorithms can be utilized to aid in the diagnosis of patients with potential COVID-19 infection. However, the accuracy of current COVID-19 recognition models is relatively low. Motivated by this fact, we propose three deep learning architectures, F-EDNC, FC-EDNC, and O-EDNC, to quickly and accurately detect COVID-19 infections from chest computed tomography (CT) images. Sixteen deep learning neural networks have been modified and trained to recognize COVID-19 patients using transfer learning and 2458 CT chest images. The proposed EDNC has then been developed using three of sixteen modified pre-trained models to improve the performance of COVID-19 recognition. The results suggested that the F-EDNC method significantly enhanced the recognition of COVID-19 infections with 97.75% accuracy, followed by FC-EDNC and O-EDNC (97.55% and 96.12%, respectively), which is superior to most of the current COVID-19 recognition models. Furthermore, a localhost web application has been built that enables users to easily upload their chest CT scans and obtain their COVID-19 results automatically. This accurate, fast, and automatic COVID-19 recognition system will relieve the stress of medical professionals for screening COVID-19 infections.

Keywords: COVID-19; CT scans; automatic recognition; deep learning; ensemble; transfer learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of COVID-19 recognition system design.
Figure 2
Figure 2
CT scans of COVID-19 and non-COVID-19 patients. (a) CT scan of a COVID-19 patient. (b) CT scan of a non-COVID-19 (not normal) patient.
Figure 3
Figure 3
CT scans of COVID-19 and non-COVID-19 patients. (a) CT scan of a COVID-19 patient. (b) CT scan of a non-COVID-19 (not normal) patient.
Figure 4
Figure 4
Average pooling procedure.
Figure 5
Figure 5
The architecture of the modified pre-trained model.
Figure 6
Figure 6
The architecture of the F-EDNC model.
Figure 7
Figure 7
The architecture of the FC-EDNC model.
Figure 8
Figure 8
The architecture of the O-EDNC model.
Figure 9
Figure 9
The architecture of the CANet model.
Figure 10
Figure 10
The architecture of the COVID-19 recognition system.
Figure 11
Figure 11
A representation of the confusion matrix.
Figure 12
Figure 12
Confusion matrix result of the main dataset for sixteen modified pre-trained models in one hold-out run.
Figure 13
Figure 13
Learning curves of the main dataset for sixteen modified pre-trained models in one hold-out run.
Figure 14
Figure 14
Confusion matrix result for EDNC and CANet models in one hold-out run.
Figure 15
Figure 15
Learning curves for EDNC and CANet models in one hold-out run.
Figure 16
Figure 16
Overview of the frontend of the localhost web application.
See this image and copyright information in PMC

References

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