| Summary, etc |
ABSTRACT<br/>Coronavirus disease 19 (COVID-19) is a pathogenic and highly transmissible virus infection, <br/>where the letters CO, VI, and D stand for corona, virus, and disease, respectively. This disease is <br/>caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) that resulted in a <br/>global pandemic and a significant loss of human life (Salvia, et al., 2021). This research explains <br/>and identifies the use of deep learning techniques (residual networks) to diagnose COVID-19 from <br/>lung ultrasound imagery. It identifies the advantages of deep learning with medical images and <br/>gives reasons why lung ultrasound is considered a viable tool for covid-19 diagnosis. Multiple <br/>journals, articles, reports...etc. on works related to deep learning for covid-19 diagnosis using lung <br/>ultrasounds were reviewed. The model was developed using the POCUS dataset and for <br/>performance analysis was compared to our baseline model (POCOVID-NET). Finally, it concludes <br/>that diagnosis of COVID-19 could be aided by deep learning approaches for computer-assisted <br/>interpretation of lung ultrasound imagery and affirms that RESNET-18 can be used to build a <br/>viable computer-assisted diagnosis method for COVID-19. |
