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Research Article
Houman Sotoudeh
University of Alabama at Birmingham
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Declarations:
Declarations
- This study involved human subjects.
- The author confirmed that all appropriate ethical guidelines for the use of human subjects have been followed, any necessary IRB and/or ethics committee review has been obtained, and information about the IRB/ethics committee is included in the manuscript.
- The author has confirmed that all necessary patient/participant consent or assent has been obtained and the appropriate institutional forms have been archived. If the IRB/ethics committee waived the requirement for patient/participant consent or assent, an explanation for the waiver is included in the text.
- The author has confirmed that a statement listing potential conflicts of interest or lack thereof is included in the text.
Objectives: It is unlikely that by fall and winter of 2020, standard vaccine or treatment is available for COVID-19 infection. In this period, differentiation between COVID-19 and Influenza induced pneumonia will be critical for patient management. To develop an automated platform to perform this task, artificial intelligence models were developed by using the transfer learning techniques on chest CT.
Methods: Chest CT images from known cases of COVID-19, H1N1 Influenza induced pneumonia (before December 2019), and normal chest CTs were collected. Different pre-trained Convolutional Neural Networks (CNN) models, including VGG 16, VGG 19, ResNet-50, Wide ResNet, InceptionV3, and SqueezNet were fine-tuned on this data set. 60% of the dataset was used for training, 20% for validation, and 20% for test the final models. Accuracy, Precision, Recall and F1 score of each model were calculated.
Results: For differentiation of COVID-19 pneumonia versus H1N1 Influenza pneumonia versus normal CTs, the ResNet-50 (accuracy above 92%) outperformed other models followed by InceptionV3 and wide ResNet.
Conclusions: The pre-trained image classification AI models are feasible to be fine-tuned and used for differentiation COVID-19 versus H1N1 Influenza pneumonia. In this context, ResNet-50 and then InceptionV3 architectures appear more promising and are suitable start points for further development. We share the source code and trained models in the supplement of this manuscript to be used by other researchers for further development.
Keywords
COVID-19, Influenza, Artificial intelligence, Machine learning
This is a list of supplementary files associated with this preprint. Click to download.
- 1ResNet20ONLYforresearch.docx
- 2ResNet20ONLYforresearch.yaml
- 3ResNet0ONLYforresearch.docx
- 4ResNet0ONLYforresearch.yaml
- 5ResNet30ONLYforresaech.docx
- 6ResNet30ONLYforresearch.yaml
- 7ResNet40ONLYforresearch.docx
- 8ResNet40ONLYforresearch.yaml
- 9InceptionV320ONLYforresearch.docx
- 10InceptionV320ONLYforresearch.yaml
- 11InceptionV330ONLYforresearch.docx
- 12InceptionV330ONLYforresearch.yaml
- 13InceptionV30ONLYforresearch.docx
- 14InceptionV30ONLYforresearch.yaml
- 15WideResNet20ONLYforresearch.docx
- 16WideResNet20ONLYforresearch.yaml
- 17WideResnet0ONLYforresearch.docx
- 18WideResNet0ONLYforresearch.yaml
- 19VGG190ONLYforresearch.docx
- 20VGG190ONLYforresearch.yaml
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This is a preprint. It has not completed peer review.
Research Article
Houman Sotoudeh
University of Alabama at Birmingham
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 14 Jul, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Declarations:
Declarations
- This study involved human subjects.
- The author confirmed that all appropriate ethical guidelines for the use of human subjects have been followed, any necessary IRB and/or ethics committee review has been obtained, and information about the IRB/ethics committee is included in the manuscript.
- The author has confirmed that all necessary patient/participant consent or assent has been obtained and the appropriate institutional forms have been archived. If the IRB/ethics committee waived the requirement for patient/participant consent or assent, an explanation for the waiver is included in the text.
- The author has confirmed that a statement listing potential conflicts of interest or lack thereof is included in the text.
Objectives: It is unlikely that by fall and winter of 2020, standard vaccine or treatment is available for COVID-19 infection. In this period, differentiation between COVID-19 and Influenza induced pneumonia will be critical for patient management. To develop an automated platform to perform this task, artificial intelligence models were developed by using the transfer learning techniques on chest CT.
Methods: Chest CT images from known cases of COVID-19, H1N1 Influenza induced pneumonia (before December 2019), and normal chest CTs were collected. Different pre-trained Convolutional Neural Networks (CNN) models, including VGG 16, VGG 19, ResNet-50, Wide ResNet, InceptionV3, and SqueezNet were fine-tuned on this data set. 60% of the dataset was used for training, 20% for validation, and 20% for test the final models. Accuracy, Precision, Recall and F1 score of each model were calculated.
Results: For differentiation of COVID-19 pneumonia versus H1N1 Influenza pneumonia versus normal CTs, the ResNet-50 (accuracy above 92%) outperformed other models followed by InceptionV3 and wide ResNet.
Conclusions: The pre-trained image classification AI models are feasible to be fine-tuned and used for differentiation COVID-19 versus H1N1 Influenza pneumonia. In this context, ResNet-50 and then InceptionV3 architectures appear more promising and are suitable start points for further development. We share the source code and trained models in the supplement of this manuscript to be used by other researchers for further development.
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