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Detection of COVID 19 using X-ray Images with Fine-tuned Transfer Learning

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Title Detection of COVID 19 using X-ray Images with Fine-tuned Transfer Learning
 
Creator Madhavi, K Reddy
Suneetha, K
Raju, K Srujan
Kora, Padmavathi
Madhavi, Gudavalli
Kallam, Suresh
 
Subject COVID 19
Transfer learning
VGG-16
X-ray
 
Description 241-248
Recently, COVID-19 infection has been spread to a wider human population worldwide and deemed a pandemic for its
rapidity. The absence of medicine or immunization for the “COVID-19” illness, along with the requirement for early
discovery and isolation of affected persons, is critical in reducing the risk of infection in healthy population. Blood
specimens, or “RT-PCR” are primary screening technique for “COVID-19”. However, average positive “RT-PCR” is
expected as 30 to 60%, leading to undiscovered infections and potentially endangering a broad population of healthy persons
with infectious symptoms. With the quick examination approach, chest radiography as a common approach for identifying
respiratory disorders is straightforward to execute. A board-certified radiologist indicated the presence of disease in these
radiographs. Four transfer learning techniques to COVID-19 illness identification were trained using 2,000 X-rays: VGG-
16, GoogleNet, ResNet, and SqueezeNet. The result of the experimental assessment shows that the VGG-16 network finetuned
with Keras achieved sensitivity of 100% with specificity of 98.5% and accuracy of approximately 99.3%.
 
Date 2023-02-08T05:11:22Z
2023-02-08T05:11:22Z
2023-02
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61360
https://doi.org/10.56042/jsir.v82i2.70216
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(02) [February 2023]