Author: Alejandro Lopez-Rincon; Alberto Tonda; Lucero Mendoza-Maldonado; Eric Claassen; Johan Garssen; Aletta D. Kraneveld
Title: Accurate Identification of SARS-CoV-2 from Viral Genome Sequences using Deep Learning Document date: 2020_3_14
ID: c2lljdi7_63
Snippet: Given the high transmissibility of the SARS-CoV-2, the proper diagnosis of 335 the disease is urgent, to stop the virus from spreading further. Considering the false negatives given by the standard nucleic acid detection, better implementations such as using deep learning are necessary in order to to properly detect the virus. While the accuracy of current nucleic acid testing is around 30-50%, and CT scans with deep learning go up at 83%, we bel.....
Document: Given the high transmissibility of the SARS-CoV-2, the proper diagnosis of 335 the disease is urgent, to stop the virus from spreading further. Considering the false negatives given by the standard nucleic acid detection, better implementations such as using deep learning are necessary in order to to properly detect the virus. While the accuracy of current nucleic acid testing is around 30-50%, and CT scans with deep learning go up at 83%, we believe that the use of the 340 sequences detected by a CNN-based system has the potential to improve the accuracy of the diagnosis above 95%.
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