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dc.contributor.authorSen, Shibaprasad
dc.contributor.authorSaha, Soumyajit
dc.contributor.authorChatterjee, Somnath
dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorSarkar, Ram
dc.date.accessioned2021-09-09T01:10:57Z
dc.date.available2021-09-09T01:10:57Z
dc.date.issued2021
dc.identifier.issn0924-669X
dc.identifier.doi10.1007/s10489-021-02292-8
dc.identifier.urihttp://hdl.handle.net/10072/407826
dc.description.abstractThe rapid spread of coronavirus disease has become an example of the worst disruptive disasters of the century around the globe. To fight against the spread of this virus, clinical image analysis of chest CT (computed tomography) images can play an important role for an accurate diagnostic. In the present work, a bi-modular hybrid model is proposed to detect COVID-19 from the chest CT images. In the first module, we have used a Convolutional Neural Network (CNN) architecture to extract features from the chest CT images. In the second module, we have used a bi-stage feature selection (FS) approach to find out the most relevant features for the prediction of COVID and non-COVID cases from the chest CT images. At the first stage of FS, we have applied a guided FS methodology by employing two filter methods: Mutual Information (MI) and Relief-F, for the initial screening of the features obtained from the CNN model. In the second stage, Dragonfly algorithm (DA) has been used for the further selection of most relevant features. The final feature set has been used for the classification of the COVID-19 and non-COVID chest CT images using the Support Vector Machine (SVM) classifier. The proposed model has been tested on two open-access datasets: SARS-CoV-2 CT images and COVID-CT datasets and the model shows substantial prediction rates of 98.39% and 90.0% on the said datasets respectively. The proposed model has been compared with a few past works for the prediction of COVID-19 cases. The supporting codes are uploaded in the Github link: https://github.com/Soumyajit-Saha/A-Bi-Stage-Feature-Selection-on-Covid-19-Dataset
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherSpringer
dc.relation.ispartofjournalApplied Intelligence
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchcode4602
dc.subject.fieldofresearchcode46
dc.subject.keywordsScience & Technology
dc.subject.keywordsComputer Science
dc.subject.keywordsCoronavirus
dc.titleA bi-stage feature selection approach for COVID-19 prediction using chest CT images
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationSen, S; Saha, S; Chatterjee, S; Mirjalili, S; Sarkar, R, A bi-stage feature selection approach for COVID-19 prediction using chest CT images, Applied Intelligence, 2021
dc.date.updated2021-09-09T00:36:17Z
gro.description.notepublicThis publication has been entered as an advanced online version in Griffith Research Online.
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali


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