Automatic scoring of COVID-19 severity in X-ray imaging based on a novel deep learning workflow.

ABSTRACT: In this study, we propose a two-stage workflow used for the segmentation and scoring of lung diseases. The workflow inherits quantification, qualification, and visual assessment of lung diseases on X-ray images estimated by radiologists and clinicians. It requires the fulfillment of two core stages devoted to lung and disease segmentation as well as an additional post-processing stage devoted to scoring. The latter integrated block is utilized, mainly, for the estimation of segment scores and computes the overall severity score of a patient. The models of the proposed workflow were trained and tested on four publicly available X-ray datasets of COVID-19 patients and two X-ray datasets of patients with no pulmonary pathology. Based on a combined dataset consisting of 580 COVID-19 patients a

SUBMITTER: Danilov VV 

PROVIDER: S-EPMC9326426 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature