Deep-learning based detection of COVID-19 using lung ultrasound imagery.

ABSTRACT:

Background

The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, especially in underdeveloped countries. There is a clear need to develop novel computer-assisted diagnosis tools to provide rapid and cost-effective screening in places where massive traditional testing is not feasible. Lung ultrasound is a portable, easy to disinfect, low cost and non-invasive tool that can be used to identify lung diseases. Computer-assisted analysis of lung ultrasound imagery is a relatively recent approach that has shown great potential for diagnosing pulmonary conditions, being a viable alternative for screening and diagnosing COVID-19.

Objective

To evaluate and compare the performance of deep-learning techniques for detecting COVID-19 infections from lung u

SUBMITTER: Diaz-Escobar J 

PROVIDER: S-EPMC8363024 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature