A comparison of Covid-19 early detection between convolutional neural networks and radiologists.

ABSTRACT:

Background

The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience.

Methods

The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal

SUBMITTER: Albiol A 

PROVIDER: S-EPMC9330942 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature