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dc.contributor.authorKundu, Rohit
dc.contributor.authorSingh, Pawan Kumar
dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorSarkar, Ram
dc.date.accessioned2021-10-18T23:28:10Z
dc.date.available2021-10-18T23:28:10Z
dc.date.issued2021
dc.identifier.issn0010-4825
dc.identifier.doi10.1016/j.compbiomed.2021.104895
dc.identifier.urihttp://hdl.handle.net/10072/409206
dc.description.abstractThe COVID-19 pandemic has collapsed the public healthcare systems, along with severely damaging the economy of the world. The SARS-CoV-2 virus also known as the coronavirus, led to community spread, causing the death of more than a million people worldwide. The primary reason for the uncontrolled spread of the virus is the lack of provision for population-wise screening. The apparatus for RT-PCR based COVID-19 detection is scarce and the testing process takes 6–9 h. The test is also not satisfactorily sensitive (71% sensitive only). Hence, Computer-Aided Detection techniques based on deep learning methods can be used in such a scenario using other modalities like chest CT-scan images for more accurate and sensitive screening. In this paper, we propose a method that uses a Sugeno fuzzy integral ensemble of four pre-trained deep learning models, namely, VGG-11, GoogLeNet, SqueezeNet v1.1 and Wide ResNet-50-2, for classification of chest CT-scan images into COVID and Non-COVID categories. The proposed framework has been tested on a publicly available dataset for evaluation and it achieves 98.93% accuracy and 98.93% sensitivity on the same. The model outperforms state-of-the-art methods on the same dataset and proves to be a reliable COVID-19 detector. The relevant source codes for the proposed approach can be found at: https://github.com/Rohit-Kundu/Fuzzy-Integral-Covid-Detection.
dc.description.peerreviewedYes
dc.languageen
dc.publisherElsevier BV
dc.relation.ispartofpagefrom104895
dc.relation.ispartofjournalComputers in Biology and Medicine
dc.relation.ispartofvolume138
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchClinical sciences
dc.subject.fieldofresearchRespiratory diseases
dc.subject.fieldofresearchcode46
dc.subject.fieldofresearchcode3202
dc.subject.fieldofresearchcode320103
dc.titleCOVID-19 detection from lung CT-Scans using a fuzzy integral-based CNN ensemble
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationKundu, R; Singh, PK; Mirjalili, S; Sarkar, R, COVID-19 detection from lung CT-Scans using a fuzzy integral-based CNN ensemble, Computers in Biology and Medicine, 2021, 138, pp. 104895
dc.date.updated2021-10-14T00:43:41Z
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali


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