Attention mechanism and mixup data augmentation for classification of COVID-19 Computed Tomography images

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dc.contributor.authorSönmez, Elena Battini
dc.date.accessioned2022-10-11T09:41:22Z
dc.date.available2022-10-11T09:41:22Z
dc.date.issued2022-09
dc.description.abstractAbstract: The Coronavirus disease is quickly spreading all over the world and the emergency situation is still out of control. Latest achievements of deep learning algorithms suggest the use of deep Convolutional Neural Network to implement a computer-aided diagnostic system for automatic classification of COVID-19 CT images. In this paper, we propose to employ a feature-wise attention layer in order to enhance the discriminative features obtained by convolutional networks. Moreover, the original performance of the network has been improved using the mixup data augmentation technique. This work compares the proposed attention-based model against the stacked attention networks, and traditional versus mixup data augmentation approaches. We deduced that feature-wise attention extension, while outperforming the stacked attention variants, achieves remarkable improvements over the baseline convolutional neural networks. That is, ResNet50 architecture extended with a feature-wise attention layer obtained 95.57% accuracy score, which, to best of our knowledge, fixes the state-of-the-art in the challenging COVID-CT dataset. © 2021 The Authorsen_US
dc.fullTextLevelFull Texten_US
dc.identifier.doi10.1016/j.jksuci.2021.07.005en_US
dc.identifier.issn1319-1578
dc.identifier.pmid38620953en_US
dc.identifier.scopus2-s2.0-85111525699en_US
dc.identifier.urihttps://hdl.handle.net/11411/4561
dc.identifier.urihttps://doi.org/10.1016/j.jksuci.2021.07.005
dc.identifier.wosWOS:000862930600018en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.issue8en_US
dc.language.isoenen_US
dc.nationalInternationalen_US
dc.numberofauthors2en_US
dc.pages6199 - 6207en_US
dc.publisherKing Saud bin Abdulaziz Universityen_US
dc.relation.ispartofJournal of King Saud University - Computer and Information Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAttentionen_US
dc.subjectClassificationen_US
dc.subjectComputed Tomography (CT) imagesen_US
dc.subjectCOVID-19en_US
dc.subjectData augmentationen_US
dc.subjectMixupen_US
dc.titleAttention mechanism and mixup data augmentation for classification of COVID-19 Computed Tomography imagesen_US
dc.typeArticleen_US
dc.volume34en_US

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