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Detection of COVID 19 Using X-Ray Images with Fine-Tuned Transfer Learning


Affiliations
1 CSE, Sree Vidyanikethan Engineering College, Tirupati 517 102, Andhra Pradesh, India
2 CS & IT, Jain (Deemed-to-be University), Bangalore 560 069, Karnataka, India
3 Department of CSE, CMR Technical Campus, Hyderabad 501 401, Telangana, India
4 Department of ECE, GRIET, Hyderabad 500 090, Telangana, India
5 JNTUK University College of Engineering, Narasaraopet, Guntur 522 601, Andhra Pradesh, India
 

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%.

Keywords

COVID 19, Transfer Learning, VGG-16, X-Ray.
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  • Detection of COVID 19 Using X-Ray Images with Fine-Tuned Transfer Learning

Abstract Views: 212  |  PDF Views: 46

Authors

K Reddy Madhavi
CSE, Sree Vidyanikethan Engineering College, Tirupati 517 102, Andhra Pradesh, India
K Suneetha
CS & IT, Jain (Deemed-to-be University), Bangalore 560 069, Karnataka, India
K Srujan Raju
Department of CSE, CMR Technical Campus, Hyderabad 501 401, Telangana, India
Padmavathi Kora
Department of ECE, GRIET, Hyderabad 500 090, Telangana, India
Gudavalli Madhavi
JNTUK University College of Engineering, Narasaraopet, Guntur 522 601, Andhra Pradesh, India
Suresh Kallam
CSE, Sree Vidyanikethan Engineering College, Tirupati 517 102, Andhra Pradesh, India

Abstract


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%.

Keywords


COVID 19, Transfer Learning, VGG-16, X-Ray.

References