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Early COVID-19 Diagnosis from Lung Ultrasound Images Combining RIULBP-TP and 3D-DenseNet
The pandemic of COVID-19 has affected the world with the high deaths rate. Early diagnosis of this disease is the bottleneck to the patient's health recovery. Its symptoms appear through the wide range of experiments especially accompany with the severe lung lesions. These lesions could be spot...
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creator | Esmaeili, Vida Feghhi, Mahmood Mohassel Omid Shahdi, Seyed |
description | The pandemic of COVID-19 has affected the world with the high deaths rate. Early diagnosis of this disease is the bottleneck to the patient's health recovery. Its symptoms appear through the wide range of experiments especially accompany with the severe lung lesions. These lesions could be spotted on the lung ultrasound data. Being non-intrusive, low cost, portable, and accurate enough are among the main pros of ultrasound imaging. However, this imaging modality most often contain variety of noises. In order to overcome this challenge, we propose a novel approach combining Rotation Invariant Uniform LBP on 3 Planes (RIULBP-TP) and 3D-DenseNet. These methods are proved to be robust against various noises. Accordingly, our method reaches outstanding results comparing to related most state-of-the-art methods. |
doi_str_mv | 10.1109/CFIS54774.2022.9756430 |
format | conference_proceeding |
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Early diagnosis of this disease is the bottleneck to the patient's health recovery. Its symptoms appear through the wide range of experiments especially accompany with the severe lung lesions. These lesions could be spotted on the lung ultrasound data. Being non-intrusive, low cost, portable, and accurate enough are among the main pros of ultrasound imaging. However, this imaging modality most often contain variety of noises. In order to overcome this challenge, we propose a novel approach combining Rotation Invariant Uniform LBP on 3 Planes (RIULBP-TP) and 3D-DenseNet. These methods are proved to be robust against various noises. 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Early diagnosis of this disease is the bottleneck to the patient's health recovery. Its symptoms appear through the wide range of experiments especially accompany with the severe lung lesions. These lesions could be spotted on the lung ultrasound data. Being non-intrusive, low cost, portable, and accurate enough are among the main pros of ultrasound imaging. However, this imaging modality most often contain variety of noises. In order to overcome this challenge, we propose a novel approach combining Rotation Invariant Uniform LBP on 3 Planes (RIULBP-TP) and 3D-DenseNet. These methods are proved to be robust against various noises. Accordingly, our method reaches outstanding results comparing to related most state-of-the-art methods.</description><subject>3D-DenseNet</subject><subject>Costs</subject><subject>COVID-19</subject><subject>Imaging</subject><subject>Lesions</subject><subject>Lung</subject><subject>lung ultrasound data</subject><subject>Pandemics</subject><subject>RIULBP-TP</subject><subject>Ultrasonic imaging</subject><issn>2771-1374</issn><isbn>1665478721</isbn><isbn>9781665478724</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2022</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkN9KwzAYxaMgOOeeQJC8QGa-JO2XXmq7aaG4oZ14N5IuLZH-kWa72NtbcFcHzu9wOBxCHoEvAXjylK7zz0ghqqXgQiwTjGIl-RW5gziefI0CrslMIAIDieqWLEL44ZxLwRUX0Yx8r8zYnmm6-cozBgnNvGn6IfhA63HoaHHqG7prj6MJw6k_0LwzjQs0HTrrez-xj3xXvGxZuaVmwjJjmeuDe3fHe3JTmza4xUXnpFyvyvSNFZvXPH0umFcamJRoRRK7OhJaxzDtr1ACclR1xKVDHWnFjcC4Smzi6ikGaFHKA2iL1lZyTh7-a71zbv87-s6M5_3lB_kHywVO7g</recordid><startdate>20220302</startdate><enddate>20220302</enddate><creator>Esmaeili, Vida</creator><creator>Feghhi, Mahmood Mohassel</creator><creator>Omid Shahdi, Seyed</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20220302</creationdate><title>Early COVID-19 Diagnosis from Lung Ultrasound Images Combining RIULBP-TP and 3D-DenseNet</title><author>Esmaeili, Vida ; Feghhi, Mahmood Mohassel ; Omid Shahdi, Seyed</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i481-337b296ef528861975c7317074f503e785840a276c9b9eff5217b733d18b7bbc3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2022</creationdate><topic>3D-DenseNet</topic><topic>Costs</topic><topic>COVID-19</topic><topic>Imaging</topic><topic>Lesions</topic><topic>Lung</topic><topic>lung ultrasound data</topic><topic>Pandemics</topic><topic>RIULBP-TP</topic><topic>Ultrasonic imaging</topic><toplevel>online_resources</toplevel><creatorcontrib>Esmaeili, Vida</creatorcontrib><creatorcontrib>Feghhi, Mahmood Mohassel</creatorcontrib><creatorcontrib>Omid Shahdi, Seyed</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore (Online service)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Esmaeili, Vida</au><au>Feghhi, Mahmood Mohassel</au><au>Omid Shahdi, Seyed</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Early COVID-19 Diagnosis from Lung Ultrasound Images Combining RIULBP-TP and 3D-DenseNet</atitle><btitle>2022 9th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS)</btitle><stitle>CFIS</stitle><date>2022-03-02</date><risdate>2022</risdate><spage>1</spage><epage>5</epage><pages>1-5</pages><eissn>2771-1374</eissn><eisbn>1665478721</eisbn><eisbn>9781665478724</eisbn><abstract>The pandemic of COVID-19 has affected the world with the high deaths rate. Early diagnosis of this disease is the bottleneck to the patient's health recovery. Its symptoms appear through the wide range of experiments especially accompany with the severe lung lesions. These lesions could be spotted on the lung ultrasound data. Being non-intrusive, low cost, portable, and accurate enough are among the main pros of ultrasound imaging. However, this imaging modality most often contain variety of noises. In order to overcome this challenge, we propose a novel approach combining Rotation Invariant Uniform LBP on 3 Planes (RIULBP-TP) and 3D-DenseNet. These methods are proved to be robust against various noises. Accordingly, our method reaches outstanding results comparing to related most state-of-the-art methods.</abstract><pub>IEEE</pub><doi>10.1109/CFIS54774.2022.9756430</doi><tpages>5</tpages></addata></record> |
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source | IEEE Xplore All Conference Series |
subjects | 3D-DenseNet Costs COVID-19 Imaging Lesions Lung lung ultrasound data Pandemics RIULBP-TP Ultrasonic imaging |
title | Early COVID-19 Diagnosis from Lung Ultrasound Images Combining RIULBP-TP and 3D-DenseNet |
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