COVID-19 classification from X-Ray images using 2D CNN
| bracu.type.group | Research Publications | |
| datacite.rights | Metadata Only | |
| dc.contributor.author | Khan, Samiha | |
| dc.contributor.author | Islam, S M Mahsanul | |
| dc.contributor.author | Nasib, Abdullah Umar | |
| dc.contributor.author | Hasnat, Fahim | |
| dc.contributor.author | Hasan, Md. Mazidul | |
| dc.contributor.author | Mim, Sumaiya | |
| dc.contributor.author | Bin Sayed, Jawad | |
| dc.contributor.author | Mehedi, Md Humaion Kabir | |
| dc.contributor.author | Iqbal, Shadab | |
| dc.contributor.author | Rasel, Annajiat Alim | |
| dc.contributor.department | Department of Computer Science and Engineering | |
| dc.date.accessioned | 2026-08-15T12:02:03Z | |
| dc.date.available | 2026-08-15T12:02:03Z | |
| dc.date.issued | 2022-01-01 | |
| dc.description.abstract | The 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.version | Published | |
| dc.format.extent | 13-18 | |
| dc.identifier.citation | S. 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.doi | 10.1109/R10-HTC54060.2022.9929517 | |
| dc.identifier.isbn | 9781665401562 | |
| dc.identifier.issn | 25727621 | |
| dc.identifier.other | 2-s2.0-85142101911 | |
| dc.identifier.uri | https://hdl.handle.net/10361/29080 | |
| dc.language.iso | en_US | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.hasversion | 10.1109/R10-HTC54060.2022.9929517 | |
| dc.relation.ispartof | IEEE Region 10 Humanitarian Technology Conference R10 Htc | |
| dc.relation.ispartofseries | IEEE Region 10 Humanitarian Technology Conference R10 Htc | |
| dc.relation.uri | https://ieeexplore.ieee.org/document/9929517 | |
| dc.rights | false | |
| dc.subject | 2D CNN | |
| dc.subject | Classification | |
| dc.subject | Coronavirus | |
| dc.subject | Deep learning | |
| dc.subject | Images | |
| dc.subject | X-RAY | |
| dc.subject.lcsh | COVID-19 (Disease). | |
| dc.subject.lcsh | Machine learning. | |
| dc.title | COVID-19 classification from X-Ray images using 2D CNN | |
| dc.type | Conference Proceeding | |
| oaire.citation.volume | 2022-September | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.identifier.scopus-author-id | 57850573500 | |
| person.identifier.scopus-author-id | 58198016600 | |
| person.identifier.scopus-author-id | 57204512879 | |
| person.identifier.scopus-author-id | 57968337400 | |
| person.identifier.scopus-author-id | 57968646500 | |
| person.identifier.scopus-author-id | 57968648300 | |
| person.identifier.scopus-author-id | 57912856700 | |
| person.identifier.scopus-author-id | 57422283000 | |
| person.identifier.scopus-author-id | 57968942200 | |
| person.identifier.scopus-author-id | 56495276900 |