000			01668nam a22001457a 4500
008			221026b ||||| |||| 00| 0 eng d
100			_aOLESENI, Abiodun Emmanuel _99571
245			_aThe Design and Implementation of an Online Counseling/ Therapy system.
250			_aMathew O. Adewole Phd.
260			_aIbafo _bComputer science _c2022
300			_ax; 55pgs.
520			_aABSTRACT Coronavirus disease 19 (COVID-19) is a pathogenic and highly transmissible virus infection, where the letters CO, VI, and D stand for corona, virus, and disease, respectively. This disease is caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) that resulted in a global pandemic and a significant loss of human life (Salvia, et al., 2021). This research explains and identifies the use of deep learning techniques (residual networks) to diagnose COVID-19 from lung ultrasound imagery. It identifies the advantages of deep learning with medical images and gives reasons why lung ultrasound is considered a viable tool for covid-19 diagnosis. Multiple journals, articles, reports...etc. on works related to deep learning for covid-19 diagnosis using lung ultrasounds were reviewed. The model was developed using the POCUS dataset and for performance analysis was compared to our baseline model (POCOVID-NET). Finally, it concludes that diagnosis of COVID-19 could be aided by deep learning approaches for computer-assisted interpretation of lung ultrasound imagery and affirms that RESNET-18 can be used to build a viable computer-assisted diagnosis method for COVID-19.
650			_aApplied science _vComputer science _99015
942			_cTHS
999			_c6943 _d6943