Efficient Medical Image Segmentation Of COVID-19 Chest CT Images Based on Deep Learning Techniques

Sanika Walvekar, Swati Shinde

20212021 International Conference on Emerging Smart Computing and Informatics (ESCI)20 citationsDOI

Abstract

Global health has been seriously threatened due to the rapid spread of the Coronavirus disease. In some cases, patients with high risk require early detection. Considering the less RT-PCR sensitivity as a screening tool, medical imaging techniques like computed tomography (CT) provide great advantages when compared. To reduce the fatality CT or X-ray image diagnosis plays an important role. To lessen the burden of radiologists in this global health crisis use of computer-aided diagnosis is crucial. As a reason, automated image segmentation is also of great benefit for clinical resolution assistance in quantitative research and health monitoring. This paper presents an approach of CT (Computed Tomography) Segmentation of lung images using the U-Net architecture.

Topics & Concepts

SegmentationCoronavirus disease 2019 (COVID-19)Image segmentationComputer scienceArtificial intelligenceComputed tomographyMedical imagingComputer visionMedicineRadiologyMedical physicsDiseasePathologyInfectious disease (medical specialty)COVID-19 diagnosis using AIRadiomics and Machine Learning in Medical ImagingAI in cancer detection