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. 2020 Aug 11:2020:8843664.
doi: 10.1155/2020/8843664. eCollection 2020.

Identifying COVID19 from Chest CT Images: A Deep Convolutional Neural Networks Based Approach

Arnab Kumar Mishra  1 , Sujit Kumar Das  1 , Pinki Roy  1 , Sivaji Bandyopadhyay  1
Affiliations

Identifying COVID19 from Chest CT Images: A Deep Convolutional Neural Networks Based Approach

Arnab Kumar Mishra et al. J Healthc Eng. .

Abstract

Coronavirus Disease (COVID19) is a fast-spreading infectious disease that is currently causing a healthcare crisis around the world. Due to the current limitations of the reverse transcription-polymerase chain reaction (RT-PCR) based tests for detecting COVID19, recently radiology imaging based ideas have been proposed by various works. In this work, various Deep CNN based approaches are explored for detecting the presence of COVID19 from chest CT images. A decision fusion based approach is also proposed, which combines predictions from multiple individual models, to produce a final prediction. Experimental results show that the proposed decision fusion based approach is able to achieve above 86% results across all the performance metrics under consideration, with average AUROC and F1-Score being 0.883 and 0.867, respectively. The experimental observations suggest the potential applicability of such Deep CNN based approach in real diagnostic scenarios, which could be of very high utility in terms of achieving fast testing for COVID19.

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Conflict of interest statement

The authors declare that there are no conflicts of interest.

Figures

Figure 1
Figure 1
(a) Examples of positive COVID19 CT scan images. (b) Examples of non-COVID19 CT scan images.
Figure 2
Figure 2
Deep CNN based decision fusion model.
Figure 3
Figure 3
Illustration of decision fusion.
Figure 4
Figure 4
Average overall behavior of each individual model and the decision fusion model.
Figure 5
Figure 5
Average Sensitivity and Specificity of the Deep CNN based prediction models.
Figure 6
Figure 6
Average Precision and Recall of the Deep CNN based prediction models.
See this image and copyright information in PMC

References

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