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<p>Detection of COVID 19 using X-ray Images with Fine-tuned Transfer Learning</p>

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Title Statement <p>Detection of COVID 19 using X-ray Images with Fine-tuned Transfer Learning</p>
 
Added Entry - Uncontrolled Name Madhavi, K Reddy; CSE, Sree Vidyanikethan Engineering College, Tirupati 517 102, Andhra Pradesh, India
Suneetha, K ; CS & IT, Jain (Deemed-to-be University), Bangalore 560 069, Karnataka, India
Raju, K Srujan; Department of CSE, CMR Technical Campus, Hyderabad 501 401, Telangana, India
Kora, Padmavathi ; Department of ECE, GRIET, Hyderabad 500 090, Telangana, India
Madhavi, Gudavalli ; JNTUK University College of Engineering, Narasaraopet, Guntur 522 601, Andhra Pradesh, India
Kallam, Suresh ; CSE, Sree Vidyanikethan Engineering College, Tirupati 517 102, Andhra Pradesh, India
 
Uncontrolled Index Term COVID 19, Transfer learning, VGG-16, X-ray
 
Summary, etc. <p>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 fine-tuned with Keras achieved sensitivity of 100% with specificity of 98.5% and accuracy of approximately 99.3%.</p>
 
Publication, Distribution, Etc. Journal of Scientific & Industrial Research
2023-02-09 21:08:13
 
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http://op.niscair.res.in/index.php/JSIR/article/view/70216
 
Data Source Entry Journal of Scientific & Industrial Research; ##issue.vol## 82, ##issue.no## 02 (2023)
 
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