COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans

In this paper we propose two novel deep convolutional network architectures, CovidResNet and CovidDenseNet, to diagnose COVID-19 based on CT images. The models enable transfer learning between different architectures, which might significantly boost the diagnostic performance. Whereas novel architec...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:PeerJ. Computer science Ročník 7; s. e655
Hlavní autoři: Alshazly, Hammam, Linse, Christoph, Abdalla, Mohamed, Barth, Erhardt, Martinetz, Thomas
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States PeerJ. Ltd 29.07.2021
PeerJ, Inc
PeerJ Inc
Témata:
Artificial Intelligence
Automated diagnosis
Automation
Bioinformatics
Cable television broadcasting industry
Classification
Computed tomography
Computer architecture
Computer Vision
Coronaviruses
COVID-19 detection
CT imaging
Data Mining and Machine Learning
Datasets
Deep learning
Health aspects
Medical imaging
Medical imaging equipment
Multi-class classification
Pneumonia
SARS-CoV-2
Sensitivity
Viral infections
United States
Taiwan
Deep learning
Multi-class classification
SARS-CoV-2
COVID-19 detection
Computed tomography
Automated diagnosis
ISSN:2376-5992, 2376-5992
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In this paper we propose two novel deep convolutional network architectures, CovidResNet and CovidDenseNet, to diagnose COVID-19 based on CT images. The models enable transfer learning between different architectures, which might significantly boost the diagnostic performance. Whereas novel architectures usually suffer from the lack of pretrained weights, our proposed models can be partly initialized with larger baseline models like ResNet50 and DenseNet121, which is attractive because of the abundance of public repositories. The architectures are utilized in a first experimental study on the SARS-CoV-2 CT-scan dataset, which contains 4173 CT images for 210 subjects structured in a subject-wise manner into three different classes. The models differentiate between COVID-19, non-COVID-19 viral pneumonia, and healthy samples. We also investigate their performance under three binary classification scenarios where we distinguish COVID-19 from healthy, COVID-19 from non-COVID-19 viral pneumonia, and non-COVID-19 from healthy, respectively. Our proposed models achieve up to 93.87% accuracy, 99.13% precision, 92.49% sensitivity, 97.73% specificity, 95.70% F1-score, and 96.80% AUC score for binary classification, and up to 83.89% accuracy, 80.36% precision, 82.04% sensitivity, 92.07% specificity, 81.05% F1-score, and 94.20% AUC score for the three-class classification tasks. We also validated our models on the COVID19-CT dataset to differentiate COVID-19 and other non-COVID-19 viral infections, and our CovidDenseNet model achieved the best performance with 81.77% accuracy, 79.05% precision, 84.69% sensitivity, 79.05% specificity, 81.77% F1-score, and 87.50% AUC score. The experimental results reveal the effectiveness of the proposed networks in automated COVID-19 detection where they outperform standard models on the considered datasets while being more efficient.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.655