COVID-19 Detection in CT Images using Deep Transfer Learning


  • A. Anbarasi Department of Computer Science, School of Engineering and Technology, Pondicherry University, Pondicherry, 605014, India.
  • K. C. Nithyasree Department of Computer Science and Engineering, Rajiv Gandhi College of Engineering and Technology, Pondicherry, 607403, India.


VGG-16, Deep learning, CT images, Convolution neural networks (CNN)


Confronting the COVID-19 pandemic introduced by newest corona virus, SARS-CoV-2, is one of the human species' most influential problems today. The fast identification and isolation of infected patients is a crucial factor in slowing down the spread of the virus. The Reverse Transcription Polymerase Chain Reaction (RT-PCR) process, one of the basic methods for COVID-19 recognition, is time-consuming in addition short-lived due to the pandemic. Deep learning applied to patients' CT images has given away hopeful results popular the identification of COVID-19 in this context. The powerful net family of CNN models using CT images to perform COVID-19 recognition is suggested in this article by VGG-16. As a consequence, COVID-19 detection was proposed as a VGG-16 model with an overall accuracy of 98.33 percent. We assume that, both in terms of productivity and efficiency, the published figures reflect modern outcomes.


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How to Cite

A. Anbarasi, & K. C. Nithyasree. (2022). COVID-19 Detection in CT Images using Deep Transfer Learning. International Transactions on Electrical Engineering and Computer Science, 1(1), 1-7. Retrieved from