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231226s2022 xx |||||o 00| ||eng c |
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7 |
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|a 10.1002/ima.22706
|2 doi
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|a pubmed25n1132.xml
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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 |
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|a A lightweight capsule network architecture for detection of COVID-19 from lung CT scans
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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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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|a © 2022 Wiley Periodicals LLC.
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|a COVID-19, a novel coronavirus, has spread quickly and produced a worldwide respiratory ailment outbreak. There is a need for large-scale screening to prevent the spreading of the disease. When compared with the reverse transcription polymerase chain reaction (RT-PCR) test, computed tomography (CT) is far more consistent, concrete, and precise in detecting COVID-19 patients through clinical diagnosis. An architecture based on deep learning has been proposed by integrating a capsule network with different variants of convolution neural networks. DenseNet, ResNet, VGGNet, and MobileNet are utilized with CapsNet to detect COVID-19 cases using lung computed tomography scans. It has found that all the four models are providing adequate accuracy, among which the VGGCapsNet, DenseCapsNet, and MobileCapsNet models have gained the highest accuracy of 99%. An Android-based app can be deployed using MobileCapsNet model to detect COVID-19 as it is a lightweight model and best suited for handheld devices like a mobile
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| 650 |
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|a Journal Article
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| 650 |
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|a COVID‐19
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| 650 |
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4 |
|a CapsNet
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| 650 |
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4 |
|a DenseNet
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| 650 |
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4 |
|a MobileNet
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| 650 |
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4 |
|a ResNet
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| 650 |
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4 |
|a VGG16
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| 650 |
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4 |
|a deep learning
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| 650 |
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4 |
|a lung CT scan
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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 32(2022), 2 vom: 15. März, Seite 419-434
|w (DE-627)NLM098193090
|x 0899-9457
|7 nnas
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| 773 |
1 |
8 |
|g volume:32
|g year:2022
|g number:2
|g day:15
|g month:03
|g pages:419-434
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|u http://dx.doi.org/10.1002/ima.22706
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