See the published version of this article at BioMedical Engineering OnLine.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Rapid Identification of COVID-19 Severity in CT Scans through Classification of Deep Features
Shujin Zhu
Nanjing University of Posts and Telecommunications
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6859-1618
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BioMedical Engineering OnLine.
Background: To implement the real-time diagnosis of the severity of patients infected with novel coronavirus 2019 (COVID-19) and guide the follow-up therapeutic treatment, We collected chest CT scans of 202 patients diagnosed with the COVID-19 from three hospitals in Anhui Province, China.
Methods: A total of 729 2D axial plan slices with 246 severe cases and 483 non-severe cases were employed in this study. Four pre-trained deep models (Inception-V3, ResNet-50, ResNet-101, DenseNet-201) with multiple classifiers (linear discriminant, linear SVM, cubic SVM, KNN and Adaboost decision tree) were applied to identify the severe and non-severe COVID-19 cases. Three validation strategies (holdout validation, 10-fold cross-validation and leave-one-out) are employed to validate the feasibility of proposed pipelines.
Results and conclusion: The experimental results demonstrate that classification of the features from pre-trained deep models show the promising application in COVID-19 screening whereas the DenseNet-201 with cubic SVM model achieved the best performance. Specifically, it achieved the highest severity classification accuracy of 95.20% and 95.34% for 10-fold cross-validation and leave-one-out, respectively. The established pipeline was able to achieve a rapid and accurate identification of the severity of COVID-19. This may assist the physicians to make more efficient and reliable decisions.
Keywords
COVID-19, Tomography, Pneumonia, Coronavirus, Deep learning
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 1
Posted 29 May, 2020
Published
On 12 Aug, 2020
No community comments so far
Editorial decision: Major revision
On 22 Jun, 2020
Review #1 received
Received 11 Jun, 2020
Review #2 received
Received 11 Jun, 2020
Reviewer #3 agreed
On 10 Jun, 2020
Review #3 received
Received 10 Jun, 2020
Reviewer #2 agreed
On 09 Jun, 2020
Reviewer #1 agreed
On 09 Jun, 2020
Reviewers invited
Invitations sent on 29 May, 2020
Editor assigned
On 26 May, 2020
Submission checks complete
On 25 May, 2020
Editor invited
On 25 May, 2020
First submitted
On 23 May, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Biomedical Engineering
Learn more about our company.
This preprint is under consideration at BioMedical Engineering OnLine. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Rapid Identification of COVID-19 Severity in CT Scans through Classification of Deep Features
Shujin Zhu
Nanjing University of Posts and Telecommunications
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6859-1618
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 1
Posted 29 May, 2020
Published
On 12 Aug, 2020
No community comments so far
Editorial decision: Major revision
On 22 Jun, 2020
Review #1 received
Received 11 Jun, 2020
Review #2 received
Received 11 Jun, 2020
Reviewer #3 agreed
On 10 Jun, 2020
Review #3 received
Received 10 Jun, 2020
Reviewer #2 agreed
On 09 Jun, 2020
Reviewer #1 agreed
On 09 Jun, 2020
Reviewers invited
Invitations sent on 29 May, 2020
Editor assigned
On 26 May, 2020
Submission checks complete
On 25 May, 2020
Editor invited
On 25 May, 2020
First submitted
On 23 May, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Biomedical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BioMedical Engineering OnLine.
Background: To implement the real-time diagnosis of the severity of patients infected with novel coronavirus 2019 (COVID-19) and guide the follow-up therapeutic treatment, We collected chest CT scans of 202 patients diagnosed with the COVID-19 from three hospitals in Anhui Province, China.
Methods: A total of 729 2D axial plan slices with 246 severe cases and 483 non-severe cases were employed in this study. Four pre-trained deep models (Inception-V3, ResNet-50, ResNet-101, DenseNet-201) with multiple classifiers (linear discriminant, linear SVM, cubic SVM, KNN and Adaboost decision tree) were applied to identify the severe and non-severe COVID-19 cases. Three validation strategies (holdout validation, 10-fold cross-validation and leave-one-out) are employed to validate the feasibility of proposed pipelines.
Results and conclusion: The experimental results demonstrate that classification of the features from pre-trained deep models show the promising application in COVID-19 screening whereas the DenseNet-201 with cubic SVM model achieved the best performance. Specifically, it achieved the highest severity classification accuracy of 95.20% and 95.34% for 10-fold cross-validation and leave-one-out, respectively. The established pipeline was able to achieve a rapid and accurate identification of the severity of COVID-19. This may assist the physicians to make more efficient and reliable decisions.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6