A modified DeepLabV3+ based semantic segmentation of chest computed tomography images for COVID-19 lung infections.

ABSTRACT: Coronavirus disease (COVID-19) affects the lives of billions of people worldwide and has destructive impacts on daily life routines, the global economy, and public health. Early diagnosis and quantification of COVID-19 infection have a vital role in improving treatment outcomes and interrupting transmission. For this purpose, advances in medical imaging techniques like computed tomography (CT) scans offer great potential as an alternative to RT-PCR assay. CT scans enable a better understanding of infection morphology and tracking of lesion boundaries. Since manual analysis of CT can be extremely tedious and time-consuming, robust automated image segmentation is necessary for clinical diagnosis and decision support. This paper proposes an efficient segmentation framework based on the modifi

SUBMITTER: Polat H 

PROVIDER: S-EPMC9349869 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature