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Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images

dc.contributor.authorPaluru, Naveen
dc.contributor.authorDayal, Aveen
dc.contributor.authorJenssen, Håvard
dc.contributor.authorSakinis, Tomas
dc.contributor.authorCenkeramaddi, Linga Reddy
dc.contributor.authorJaya, Prakash
dc.contributor.authorYalavarthy, Phaneendra K.
dc.date.accessioned2022-04-22T11:55:51Z
dc.date.available2022-04-22T11:55:51Z
dc.date.created2021-06-04T14:53:53Z
dc.date.issued2021
dc.identifier.citationPaluru, N., Dayal, A., Jenssen, H., Sakinis, T., Cenkeramaddi, L.R., Jaya, P. & Yalavarthy, P.K. (2021) Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images IEEE Transactions on Neural Networks and Learning Systems. 2021, 32 (3), 932-946.en_US
dc.identifier.issn2162-237X
dc.identifier.urihttps://hdl.handle.net/11250/2992293
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.titleAnam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Imagesen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionsubmittedVersionen_US
dc.rights.holder© 2021 The Author(s)en_US
dc.subject.nsiVDP::Technology: 500en_US
dc.source.pagenumber932-946en_US
dc.source.volume32en_US
dc.source.journalIEEE Transactions on Neural Networks and Learning Systemsen_US
dc.source.issue3en_US
dc.identifier.doi10.1109/TNNLS.2021.3054746
dc.identifier.cristin1913815
dc.relation.projectNorges forskningsråd: 287918en_US
cristin.qualitycode2


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