Detection of COVID-19 from X-Ray Images

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dc.contributor.author Kandalaft, Joseph
dc.date.accessioned 2022-06-15T06:03:09Z
dc.date.available 2022-06-15T06:03:09Z
dc.date.copyright 2021 en_US
dc.date.issued 2021-08-05
dc.identifier.uri http://hdl.handle.net/10725/13665
dc.description.abstract Corona Virus Disease 2019 (COVID-19) is a new disease that is based on the SARSCoV- 2 virus. The virus has caused a worldwide pandemic due to its high infection rate and severity of symptoms. Several methods for detecting the virus exist among which different medical imaging modalities, in particular X-Ray imaging. In this thesis, we propose a three-phase machine learning approach to detect, from X-Ray images, whether a person is infected with the virus or not. The approach relies on an ensemble of customized convolutional neural networks to extract essential features from input images. The extracted features undergo fusion and are then passed on to a classifier for final results. We validated the approach on a set of 3,886 X-Ray images of patients carrying the virus, patients suffering from viral pneumonia, and healthy persons. When benchmarked against several models published in the literature, our proposed model outperformed them all. en_US
dc.language.iso en en_US
dc.subject COVID-19 (Disease) -- Diagnosis -- Computer networks en_US
dc.subject Diagnostic imaging en_US
dc.subject X-rays en_US
dc.subject Artificial intelligence -- Medical applications en_US
dc.subject Lebanese American University -- Dissertations en_US
dc.subject Dissertations, Academic en_US
dc.title Detection of COVID-19 from X-Ray Images en_US
dc.type Thesis en_US
dc.title.subtitle An Approach Combining Multiple Convolutional Neural Networks with Feature Extraction and Fusion en_US
dc.term.submitted Summer en_US
dc.author.degree MS in Computer Science en_US
dc.author.school SAS en_US
dc.author.idnumber 201002581 en_US
dc.author.commembers Abu Khzam, Faisal
dc.author.commembers Rebehmed, Joseph
dc.author.commembers Khazen, Georges
dc.author.department Computer Science And Mathematics en_US
dc.description.physdesc 1 online resource (xii, 174 leaves): ill. (some col.) en_US
dc.author.advisor Azar, Danielle
dc.keywords Convolutional Neural Networks en_US
dc.keywords Deep Learning en_US
dc.keywords Medical Imaging en_US
dc.keywords Image Classification en_US
dc.keywords X-Rays en_US
dc.keywords COVID-19 en_US
dc.description.bibliographiccitations Includes bibliographical references (leaf 76-88) en_US
dc.identifier.doi https://doi.org/10.26756/th.2022.247
dc.author.email joseph.kandalaft@lau.edu.lb en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php en_US
dc.publisher.institution Lebanese American University en_US
dc.author.affiliation Lebanese American University en_US

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