TY - JOUR AU - A. Anbarasi, AU - K. C. Nithyasree, PY - 2022/09/30 Y2 - 2024/03/29 TI - COVID-19 Detection in CT Images using Deep Transfer Learning JF - International Transactions on Electrical Engineering and Computer Science JA - International Transactions on Electrical Engineering and Computer Science VL - 1 IS - 1 SE - Articles DO - 10.62760/iteecs.1.1.2022.6 UR - https://iteecs.com/index.php/iteecs/article/view/6 SP - 1-7 AB - <p>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.</p> ER -