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|a 10.1002/ima.22770
|2 doi
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|a eng
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| 100 |
1 |
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|a Kumar, Sachin
|e verfasserin
|4 aut
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| 245 |
1 |
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|a LiteCovidNet
|b A lightweight deep neural network model for detection of COVID-19 using X-ray images
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 13.10.2022
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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 The syndrome called COVID-19 which was firstly spread in Wuhan, China has already been declared a globally "Pandemic." To stymie the further spread of the virus at an early stage, detection needs to be done. Artificial Intelligence-based deep learning models have gained much popularity in the detection of many diseases within the confines of biomedical sciences. In this paper, a deep neural network-based "LiteCovidNet" model is proposed that detects COVID-19 cases as the binary class (COVID-19, Normal) and the multi-class (COVID-19, Normal, Pneumonia) bifurcated based on chest X-ray images of the infected persons. An accuracy of 100% and 98.82% is achieved for binary and multi-class classification respectively which is competitive performance as compared to the other recent related studies. Hence, our methodology can be used by health professionals to validate the detection of COVID-19 infected patients at an early stage with convenient cost and better accuracy
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|a Journal Article
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|a COVID‐19
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| 650 |
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|a LiteCovidNet
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| 650 |
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4 |
|a chest X‐ray
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| 650 |
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|a classification
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| 650 |
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4 |
|a deep neural network
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| 700 |
1 |
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|a Shastri, Sourabh
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Mahajan, Shilpa
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Singh, Kuljeet
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gupta, Surbhi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Rani, Rajneesh
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Mohan, Neeraj
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Mansotra, Vibhakar
|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), 5 vom: 25. Sept., Seite 1464-1480
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|x 0899-9457
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|g volume:32
|g year:2022
|g number:5
|g day:25
|g month:09
|g pages:1464-1480
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|u http://dx.doi.org/10.1002/ima.22770
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