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|a 10.1002/ima.22566
|2 doi
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|a eng
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| 100 |
1 |
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|a Tiwari, Shamik
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Convolutional capsule network for COVID-19 detection using radiography images
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| 264 |
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|c 2021
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| 336 |
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 16.07.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a © 2021 Wiley Periodicals LLC.
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|a 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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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a COVID‐19
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| 650 |
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4 |
|a X‐ray
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| 650 |
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4 |
|a capsule network
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| 650 |
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4 |
|a convolutional neural network
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| 650 |
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4 |
|a decision support system
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| 650 |
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4 |
|a deep learning
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| 650 |
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4 |
|a visual geometry group
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| 700 |
1 |
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|a Jain, Anurag
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t International journal of imaging systems and technology
|d 1990
|g 31(2021), 2 vom: 30. Juni, Seite 525-539
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|g volume:31
|g year:2021
|g number:2
|g day:30
|g month:06
|g pages:525-539
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|u http://dx.doi.org/10.1002/ima.22566
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