Abstract
The present world is suffering from the severe attack of corona virus disease (COVID-19). With the view to minimize the spread of this deadly disease, testing and analysis of tremendous amounts of suspected cases for isolation of such individual and further treatment are a need. Pathogenic laboratory testing is the analytic best quality level; however, it is tedious with critical false negative outcomes. Fast and exact characteristic techniques are fundamentally expected to fight the deadly disease. Considering COVID-19 radio graphical changes in CT images, the paper centers to develop a deep learning model that could isolate COVID-19 cases in order to give a clinical end before the pathogenic test, along these lines sparing urgent time for affliction control.
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Sen, A.P., Rout, N.K. (2021). Implementation of Transfer Learning Technique for the Detection of COVID-19. In: Sabut, S.K., Ray, A.K., Pati, B., Acharya, U.R. (eds) Proceedings of International Conference on Communication, Circuits, and Systems. Lecture Notes in Electrical Engineering, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-33-4866-0_17
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DOI: https://doi.org/10.1007/978-981-33-4866-0_17
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