A Review of Deep Learning Techniques to Diagnose Covid-19 Disease


A Review of Deep Learning Techniques to Diagnose Covid-19 Disease

Sonam Saraf, Sumit Nema

Sonam Saraf, Sumit Nema "A Review of Deep Learning Techniques to Diagnose Covid-19 Disease " Published in International Journal of Trend in Research and Development (IJTRD), ISSN: 2394-9333, Conference Proceeding | NCUACC-2021 , May 2021, URL: http://www.ijtrd.com/papers/IJTRD22750.pdf

In recent decades, a number of new diseases have emerged, among which include the Ebola virus, Zika virus, Nipah virus, and Coronavirus. Recently, a new type of viral infection emerged in Wuhan City, China, and preliminary genomic sequencing data for this virus do not coincide with previously sequenced COVs, suggesting a novel COV strain (2019-NOCV), Which is now called severe acute respiratory syndrome CoV-2 (SARS-CoV-2). Compared to previously known diseases caused by human COV, COVID-19 exhibits higher transmission capacity, as evident by the number of cases continuously increasing globally. In the last 2 years, many people have died all over the world since Covid-19. This disease is spreading rapidly and it is becoming very difficult to stop it. Nowadays deep learning techniques are evolving in medical science. In this paper, we will study various types of deep learning techniques to diagnose Covid disease in the early stage.

Review, Deep Learning; Machine Learning; Algorithms; Covid-19; Diagnosis; X-ray;


Conference Proceeding | NCUACC-2021 , May 2021

2394-9333

IJTRD22750
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