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

• Published in: PeerJ. Computer science Vol. 7; p. e655
• Main Authors: Alshazly, Hammam, Linse, Christoph, Abdalla, Mohamed, Barth, Erhardt, Martinetz, Thomas
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 29.07.2021; PeerJ, Inc; PeerJ Inc
• Subjects: 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
• DOI: 10.7717/peerj-cs.655

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.