COVID-19 classification from X-Ray images using 2D CNN

bracu.type.groupResearch Publications
datacite.rightsMetadata Only
dc.contributor.authorKhan, Samiha
dc.contributor.authorIslam, S M Mahsanul
dc.contributor.authorNasib, Abdullah Umar
dc.contributor.authorHasnat, Fahim
dc.contributor.authorHasan, Md. Mazidul
dc.contributor.authorMim, Sumaiya
dc.contributor.authorBin Sayed, Jawad
dc.contributor.authorMehedi, Md Humaion Kabir
dc.contributor.authorIqbal, Shadab
dc.contributor.authorRasel, Annajiat Alim
dc.contributor.departmentDepartment of Computer Science and Engineering
dc.date.accessioned2026-08-15T12:02:03Z
dc.date.available2026-08-15T12:02:03Z
dc.date.issued2022-01-01
dc.description.abstractThe coronavirus (COVID-19) detection has been a crucial task for researchers, scientists, health experts all across the world and everyone is trying together to find a solution to it. The X-rays images of lungs have become one of the most prevalent and effective procedures used by researchers to monitor COVID-19. Unfortunately, inspecting each case involves multiple radiology experts and time, which is one of the critical tasks in such an outbreak. In this paper, a deep learning approach, 2D convolutional neural networks (CNN) has been used to classify healthy and COVID-19 chest X-ray images. 'Curated Dataset for COVID-19 Posterior-Anterior Chest Radiography Images (X-Rays)' dataset has been used in this study. The major indicator of this study is the accuracy of the proposed model. The classification model, 2D CNN has achieved accuracy and f1-score of 0.96 and 0.95 respectively.
dc.description.versionPublished
dc.format.extent13-18
dc.identifier.citationS. Khan et al., "COVID-19 Classification from X-Ray Images using 2D CNN," 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC), Hyderabad, India, 2022, pp. 13-18, doi: 10.1109/R10-HTC54060.2022.9929517.
dc.identifier.doi10.1109/R10-HTC54060.2022.9929517
dc.identifier.isbn9781665401562
dc.identifier.issn25727621
dc.identifier.other2-s2.0-85142101911
dc.identifier.urihttps://hdl.handle.net/10361/29080
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.hasversion10.1109/R10-HTC54060.2022.9929517
dc.relation.ispartofIEEE Region 10 Humanitarian Technology Conference R10 Htc
dc.relation.ispartofseriesIEEE Region 10 Humanitarian Technology Conference R10 Htc
dc.relation.urihttps://ieeexplore.ieee.org/document/9929517
dc.rightsfalse
dc.subject2D CNN
dc.subjectClassification
dc.subjectCoronavirus
dc.subjectDeep learning
dc.subjectImages
dc.subjectX-RAY
dc.subject.lcshCOVID-19 (Disease).
dc.subject.lcshMachine learning.
dc.titleCOVID-19 classification from X-Ray images using 2D CNN
dc.typeConference Proceeding
oaire.citation.volume2022-September
person.affiliation.nameBRAC University
person.affiliation.nameBRAC University
person.affiliation.nameBRAC University
person.affiliation.nameBRAC University
person.affiliation.nameBRAC University
person.affiliation.nameBRAC University
person.affiliation.nameBRAC University
person.affiliation.nameBRAC University
person.affiliation.nameBRAC University
person.affiliation.nameBRAC University
person.identifier.scopus-author-id57850573500
person.identifier.scopus-author-id58198016600
person.identifier.scopus-author-id57204512879
person.identifier.scopus-author-id57968337400
person.identifier.scopus-author-id57968646500
person.identifier.scopus-author-id57968648300
person.identifier.scopus-author-id57912856700
person.identifier.scopus-author-id57422283000
person.identifier.scopus-author-id57968942200
person.identifier.scopus-author-id56495276900

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Demo.jpg
Size:
27.35 KB
Format:
Joint Photographic Experts Group/JPEG File Interchange Format (JFIF)

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: