Development and validation of bone-suppressed deep learning classification of COVID-19 presentation in chest radiographs.

ABSTRACT:

Background

Coronavirus disease 2019 (COVID-19) is a pandemic disease. Fast and accurate diagnosis of COVID-19 from chest radiography may enable more efficient allocation of scarce medical resources and hence improved patient outcomes. Deep learning classification of chest radiographs may be a plausible step towards this. We hypothesize that bone suppression of chest radiographs may improve the performance of deep learning classification of COVID-19 phenomena in chest radiographs.

Methods

Two bone suppression methods (Gusarev et al. and Rajaraman et al.) were implemented. The Gusarev and Rajaraman methods were trained on 217 pairs of normal and bone-suppressed chest radiographs from the X-ray Bone Shadow Suppression dataset (https://www.kaggle.com/hmchuong/xray-

SUBMITTER: Lam NFD 

PROVIDER: S-EPMC9246721 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature