Convolutional capsule network for COVID-19 detection using radiography images
© 2021 Wiley Periodicals LLC.
| Veröffentlicht in: | International journal of imaging systems and technology. - 1990. - 31(2021), 2 vom: 30. Juni, Seite 525-539 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
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2021
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| Zugriff auf das übergeordnete Werk: | International journal of imaging systems and technology |
| Schlagworte: | Journal Article COVID‐19 X‐ray capsule network convolutional neural network decision support system deep learning visual geometry group |
| Zusammenfassung: | © 2021 Wiley Periodicals LLC. Novel corona virus COVID-19 has spread rapidly all over the world. Due to increasing COVID-19 cases, there is a dearth of testing kits. Therefore, there is a severe need for an automatic recognition system as a solution to reduce the spreading of the COVID-19 virus. This work offers a decision support system based on the X-ray image to diagnose the presence of the COVID-19 virus. A deep learning-based computer-aided decision support system will be capable to differentiate between COVID-19 and pneumonia. Recently, convolutional neural network (CNN) is designed for the diagnosis of COVID-19 patients through chest radiography (or chest X-ray, CXR) images. However, due to the usage of CNN, there are some limitations with these decision support systems. These systems suffer with the problem of view-invariance and loss of information due to down-sampling. In this paper, the capsule network (CapsNet)-based system named visual geometry group capsule network (VGG-CapsNet) for the diagnosis of COVID-19 is proposed. Due to the usage of capsule network (CapsNet), the authors have succeeded in removing the drawbacks found in the CNN-based decision support system for the detection of COVID-19. Through simulation results, it is found that VGG-CapsNet has performed better than the CNN-CapsNet model for the diagnosis of COVID-19. The proposed VGG-CapsNet-based system has shown 97% accuracy for COVID-19 versus non-COVID-19 classification, and 92% accuracy for COVID-19 versus normal versus viral pneumonia classification. Proposed VGG-CapsNet-based system available at https://github.com/shamiktiwari/COVID19_Xray can be used to detect the existence of COVID-19 virus in the human body through chest radiographic images |
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| Beschreibung: | Date Revised 16.07.2022 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 0899-9457 |
| DOI: | 10.1002/ima.22566 |