The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks
Open accessAbstract
Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requiring a well-equipped laboratory for analysis. Computed tomography (CT) has thus far become a fast method to diagnose patients with COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was moderate. Accordingly, additional investigations are needed to improve the performance in diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19 diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known convolutional neural networks were used to distinguish infection of COVID-19 from non-COVID-19 groups: AlexNet, VGG-16, VGG-19, SqueezeNet, GoogleNet, MobileNet-V2, ResNet-18, ResNet-50, ResNet-101, and Xception. Among all networks, the best performance was achieved by ResNet-101 and Xception. ResNet-101 could distinguish COVID-19 from non-COVID-19 cases with an AUC of 0.994 (sensitivity, 100%; specificity, 99.02%; accuracy, 99.51%). Xception achieved an AUC of 0.994 (sensitivity, 98.04%; specificity, 100%; accuracy, 99.02%). However, the performance of the radiologist was moderate with an AUC of 0.873 (sensitivity, 89.21%; specificity, 83.33%; accuracy, 86.27%). ResNet-101 can be considered as a high sensitivity model to characterize and diagnose COVID-19 infections, and can be used as an adjuvant tool in radiology departments.
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Cited by
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Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. (2020) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110059 · Cited by 845
Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays
Brunese, L., Mercaldo, F., Reginelli, A., & Santone, A. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105608 · Cited by 623
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Metadata
- Authors
- Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., & Mohammadi, A.
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 121
- Pages
- 103795
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W3017644243
- Cited by
- 851
- References
- 10
- FWCI
- 81.36
- Type
- journal article
- Language
- en
- Open access
- green
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- Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., & Mohammadi, A. (2020). Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Computers in Biology and Medicine. https://doi.org/10.1016/j.compbiomed.2020.103795
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