Show simple item record

dc.contributor.authorKundu, Rohit
dc.contributor.authorSingh, Pawan Kumar
dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorSarkar, Ram
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:
dc.publisherElsevier BV
dc.relation.ispartofjournalComputers in Biology and Medicine
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchClinical sciences
dc.subject.fieldofresearchRespiratory diseases
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
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record