Review and analysis of COVID-19 lung lesion segmentation technique and algorithms

S. Salini; B. Persis Urbana Ivy

doi:10.53730/ijhs.v6nS2.7206

Authors

S. Salini M.E (Computer Engineering)., Research Scholar., Bharath Institute of Higher Education and Research, Chennai
B. Persis Urbana Ivy M.E., Ph.D., Professor & Head, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai

Keywords:

COVID-19, lung disease, CT imaging

Abstract

COVID-19 is a rapidly spreading disease over the world, yet hospital resources are limited. A recent COVID-19 study reveals that CT imaging can be used to detect disease progression and aid diagnosis, as well as to better understand the disease. Many new studies suggest that deep learning could be used to swiftly and effectively detect COVID-19 in chest CT scans. The problem of automatically segmenting the lungs in CT scans of individuals with confirmed or suspected COVID-19 is intriguing. CT chest scan images allow us to analyze and categorize COVID-19's normal and abnormal properties in a comprehensive way. Segmenting and detecting suspicious areas in the images is done by the platform, which then assesses these regions to get an accurate categorization. Deep learning models can benefit from broad receptive fields that can learn contextual information and COVID-19 infection-related features for more accurate segmentation results. Deep learning models. More than 1800 annotated CT slices were used to construct and evaluate LungINFseg. For this purpose, we tested LungINFseg against 13 current deep learning-based segmentation techniques.

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