Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/569013
Title: Classification and segmentation of covid 19 ground glass opacification in lung ct images using deep learning techniques
Researcher: Arockia Sukanya,S
Guide(s): Kamalanand,K
Keywords: Engineering
Engineering and Technology
Engineering Electrical and Electronic
University: Anna University
Completed Date: 2024
Abstract: SARS-type respiratory tract infections have become one of the predominant health conditions in recent years. These infections involve the upper and lower respiratory systems. Isolation of infected persons and effective screening is a critical step in fighting against the COVID-19 disease. RTPCR test, considered one of the standard procedures for COVID-19 diagnosis, has its drawbacks, including a long wait time for obtaining the test results. Hence, early detection of COVID-19 using imaging techniques such as X-rays and CT are used for fast screening. Image processing techniques such as classification and segmentation play a significant role in biomedical analysis. Recently, deep learning techniques have been widely utilized in medical image processing. newline
Pagination: xx,130p.
URI: http://hdl.handle.net/10603/569013
Appears in Departments:Faculty of Electrical Engineering

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02_prelim_pages.pdf4.71 MBAdobe PDFView/Open
03_content.pdf217.81 kBAdobe PDFView/Open
04_abstract.pdf197.76 kBAdobe PDFView/Open
05_chapter1.pdf487.91 kBAdobe PDFView/Open
06_chapter2.pdf368.99 kBAdobe PDFView/Open
07_chapter3.pdf1.65 MBAdobe PDFView/Open
08_chapter4.pdf5.35 MBAdobe PDFView/Open
09_chapter6.pdf181 kBAdobe PDFView/Open
10_annexures.pdf626.22 kBAdobe PDFView/Open
80_recommendation.pdf53.28 kBAdobe PDFView/Open
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