Publikation: XCOVNet: Chest X-ray Image Classificati...
Stammdaten
Titel: | XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks |
Untertitel: | |
Kurzfassung: | AbstractCOVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification. |
Schlagworte: | Coronavirus, SARS-COV-2, COVID-19 disease diagnosis, Machine learning, Image classification |
Publikationstyp: | Beitrag in Zeitschrift (Autorenschaft) |
Erscheinungsdatum: | 24.02.2021 (Online) |
Erschienen in: |
New Generation Computing
New Generation Computing
(
Springer;
)
zur Publikation |
Titel der Serie: | - |
Bandnummer: | - |
Heftnummer: | - |
Erstveröffentlichung: | Ja |
Version: | - |
Seite: | S. 1 - 15 |
Versionen
Keine Version vorhanden |
Erscheinungsdatum: | 24.02.2021 |
ISBN (e-book): | - |
eISSN: | 1882-7055 |
DOI: | http://dx.doi.org/10.1007/s00354-021-00121-7 |
Homepage: | https://link.springer.com/article/10.1007/s00354-021-00121-7 |
Open Access |
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AutorInnen
Vishu Madaan
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Aditya Roy (extern) | ||||
Charu Gupta (extern) | ||||
Prateek Agrawal (intern) | ||||
Anand Sharma (extern) | ||||
Cristian Bologa (extern) | ||||
Radu Aurel Prodan (intern) |
Zuordnung
Organisation | Adresse | ||||
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
Kategorisierung
Sachgebiete | |
Forschungscluster | Kein Forschungscluster ausgewählt |
Zitationsindex |
Informationen zum Zitationsindex: Master Journal List
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Peer Reviewed |
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Publikationsfokus |
Klassifikationsraster der zugeordneten Organisationseinheiten:
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Kooperationen
Organisation | Adresse | ||||
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Lovely Professional University
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IN Phagwara, Punjab |
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Bhagwan Parshuram Institute of Technology (BPIT)
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IN - 110089 New Delhi |
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Mody University of Science and Technology
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IN - 332311 Rajasthan |
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Babes-Bolyai University
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RO - 400000 Cluj-Napoca |
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Publikationen: | Keine verknüpften Publikationen vorhanden |
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