Published in

International Journal of Advanced Research in Science, Communication and Technology, p. 836-841, 2022

DOI: 10.48175/ijarsct-4145

Institute of Electrical and Electronics Engineers, IEEE Access, (8), p. 149808-149824, 2020

DOI: 10.1109/access.2020.3016780

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COVID-19 Detection through Transfer Learning using Multimodal Imaging Data

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Late Reverse Transcription - Polymerase Chain Reaction (RT-PCR) structure for the acknowledgment of COVID-19 needs behind as a result of limited openness of test units and for the most part low certain signs in the first place periods of the disease, empowering the necessity for elective plans. To foster COVID-19 infection forecast instrument in light of Artificial Intelligence might benefit from some intervention. In the proposed framework CT pictures of X-beams are prepared utilizing Convolutional Neural Network strategies, which can assist framework with naturally anticipating the COVID-19 identification. The precision has demonstrated to be higher than different methods. Our proposed framework can accomplish close by 93-94% of precision for recognition of Coronavirus on bases of X-beam.