Classification of COVID‐19 and Influenza Patients Using Deep Learning Article Swipe
Muhammad Umar Aftab
,
Rashid Amin
,
Deepika Koundal
,
Hamza Aldabbas
,
Bader Alouffi
,
Zeshan Iqbal
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/8549707
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/8549707
Coronavirus (COVID‐19) is a deadly virus that initially starts with flu‐like symptoms. COVID‐19 emerged in China and quickly spread around the globe, resulting in the coronavirus epidemic of 2019–22. As this virus is very similar to influenza in its early stages, its accurate detection is challenging. Several techniques for detecting the virus in its early stages are being developed. Deep learning techniques are a handy tool for detecting various diseases. For the classification of COVID‐19 and influenza, we proposed tailored deep learning models. A publicly available dataset of X‐ray images was used to develop proposed models. According to test results, deep learning models can accurately diagnose normal, influenza, and COVID‐19 cases. Our proposed long short‐term memory (LSTM) technique outperformed the CNN model in the evaluation phase on chest X‐ray images, achieving 98% accuracy.
Related Topics
Concepts
Coronavirus disease 2019 (COVID-19)
Virology
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
2019-20 coronavirus outbreak
Medicine
Internal medicine
Infectious disease (medical specialty)
Outbreak
Disease
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/8549707
- https://downloads.hindawi.com/journals/cmmi/2022/8549707.pdf
- OA Status
- hybrid
- Cited By
- 17
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4214656236
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4214656236Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2022/8549707Digital Object Identifier
- Title
-
Classification of COVID‐19 and Influenza Patients Using Deep LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Muhammad Umar Aftab, Rashid Amin, Deepika Koundal, Hamza Aldabbas, Bader Alouffi, Zeshan IqbalList of authors in order
- Landing page
-
https://doi.org/10.1155/2022/8549707Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/cmmi/2022/8549707.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://downloads.hindawi.com/journals/cmmi/2022/8549707.pdfDirect OA link when available
- Concepts
-
Coronavirus disease 2019 (COVID-19), Virology, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 2019-20 coronavirus outbreak, Medicine, Internal medicine, Infectious disease (medical specialty), Outbreak, DiseaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
17Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 4, 2023: 6, 2022: 5Per-year citation counts (last 5 years)
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4214656236 |
|---|---|
| doi | https://doi.org/10.1155/2022/8549707 |
| ids.doi | https://doi.org/10.1155/2022/8549707 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/35280712 |
| ids.openalex | https://openalex.org/W4214656236 |
| fwci | 3.31924171 |
| mesh[0].qualifier_ui | Q000145 |
| mesh[0].descriptor_ui | D000086382 |
| mesh[0].is_major_topic | True |
| mesh[0].qualifier_name | classification |
| mesh[0].descriptor_name | COVID-19 |
| mesh[1].qualifier_ui | Q000000981 |
| mesh[1].descriptor_ui | D000086382 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | diagnostic imaging |
| mesh[1].descriptor_name | COVID-19 |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D000077321 |
| mesh[2].is_major_topic | True |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Deep Learning |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D005260 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Female |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D006801 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Humans |
| mesh[5].qualifier_ui | Q000145 |
| mesh[5].descriptor_ui | D007251 |
| mesh[5].is_major_topic | True |
| mesh[5].qualifier_name | classification |
| mesh[5].descriptor_name | Influenza, Human |
| mesh[6].qualifier_ui | Q000000981 |
| mesh[6].descriptor_ui | D007251 |
| mesh[6].is_major_topic | True |
| mesh[6].qualifier_name | diagnostic imaging |
| mesh[6].descriptor_name | Influenza, Human |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D008297 |
| mesh[7].is_major_topic | False |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Male |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D000086402 |
| mesh[8].is_major_topic | True |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | SARS-CoV-2 |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D014057 |
| mesh[9].is_major_topic | True |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Tomography, X-Ray Computed |
| mesh[10].qualifier_ui | Q000145 |
| mesh[10].descriptor_ui | D000086382 |
| mesh[10].is_major_topic | True |
| mesh[10].qualifier_name | classification |
| mesh[10].descriptor_name | COVID-19 |
| mesh[11].qualifier_ui | Q000000981 |
| mesh[11].descriptor_ui | D000086382 |
| mesh[11].is_major_topic | True |
| mesh[11].qualifier_name | diagnostic imaging |
| mesh[11].descriptor_name | COVID-19 |
| mesh[12].qualifier_ui | |
| mesh[12].descriptor_ui | D000077321 |
| mesh[12].is_major_topic | True |
| mesh[12].qualifier_name | |
| mesh[12].descriptor_name | Deep Learning |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D005260 |
| mesh[13].is_major_topic | False |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Female |
| mesh[14].qualifier_ui | |
| mesh[14].descriptor_ui | D006801 |
| mesh[14].is_major_topic | False |
| mesh[14].qualifier_name | |
| mesh[14].descriptor_name | Humans |
| mesh[15].qualifier_ui | Q000145 |
| mesh[15].descriptor_ui | D007251 |
| mesh[15].is_major_topic | True |
| mesh[15].qualifier_name | classification |
| mesh[15].descriptor_name | Influenza, Human |
| mesh[16].qualifier_ui | Q000000981 |
| mesh[16].descriptor_ui | D007251 |
| mesh[16].is_major_topic | True |
| mesh[16].qualifier_name | diagnostic imaging |
| mesh[16].descriptor_name | Influenza, Human |
| mesh[17].qualifier_ui | |
| mesh[17].descriptor_ui | D008297 |
| mesh[17].is_major_topic | False |
| mesh[17].qualifier_name | |
| mesh[17].descriptor_name | Male |
| mesh[18].qualifier_ui | |
| mesh[18].descriptor_ui | D000086402 |
| mesh[18].is_major_topic | True |
| mesh[18].qualifier_name | |
| mesh[18].descriptor_name | SARS-CoV-2 |
| mesh[19].qualifier_ui | |
| mesh[19].descriptor_ui | D014057 |
| mesh[19].is_major_topic | True |
| mesh[19].qualifier_name | |
| mesh[19].descriptor_name | Tomography, X-Ray Computed |
| type | article |
| title | Classification of COVID‐19 and Influenza Patients Using Deep Learning |
| awards[0].id | https://openalex.org/G8388568442 |
| awards[0].funder_id | https://openalex.org/F4320323722 |
| awards[0].display_name | |
| awards[0].funder_award_id | TURSP-2020/314 |
| awards[0].funder_display_name | Taif University |
| biblio.issue | 1 |
| biblio.volume | 2022 |
| biblio.last_page | 8549707 |
| biblio.first_page | 8549707 |
| topics[0].id | https://openalex.org/T11775 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2741 |
| topics[0].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[0].display_name | COVID-19 diagnosis using AI |
| topics[1].id | https://openalex.org/T11512 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9959999918937683 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Anomaly Detection Techniques and Applications |
| topics[2].id | https://openalex.org/T13702 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9915000200271606 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Machine Learning in Healthcare |
| funders[0].id | https://openalex.org/F4320323722 |
| funders[0].ror | https://ror.org/014g1a453 |
| funders[0].display_name | Taif University |
| is_xpac | False |
| apc_list.value | 1350 |
| apc_list.currency | USD |
| apc_list.value_usd | 1350 |
| apc_paid.value | 1350 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 1350 |
| concepts[0].id | https://openalex.org/C3008058167 |
| concepts[0].level | 4 |
| concepts[0].score | 0.8197652697563171 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q84263196 |
| concepts[0].display_name | Coronavirus disease 2019 (COVID-19) |
| concepts[1].id | https://openalex.org/C159047783 |
| concepts[1].level | 1 |
| concepts[1].score | 0.6414763331413269 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q7215 |
| concepts[1].display_name | Virology |
| concepts[2].id | https://openalex.org/C3007834351 |
| concepts[2].level | 5 |
| concepts[2].score | 0.5871832370758057 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q82069695 |
| concepts[2].display_name | Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) |
| concepts[3].id | https://openalex.org/C3006700255 |
| concepts[3].level | 3 |
| concepts[3].score | 0.5819908976554871 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q81068910 |
| concepts[3].display_name | 2019-20 coronavirus outbreak |
| concepts[4].id | https://openalex.org/C71924100 |
| concepts[4].level | 0 |
| concepts[4].score | 0.4251735210418701 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[4].display_name | Medicine |
| concepts[5].id | https://openalex.org/C126322002 |
| concepts[5].level | 1 |
| concepts[5].score | 0.13189733028411865 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[5].display_name | Internal medicine |
| concepts[6].id | https://openalex.org/C524204448 |
| concepts[6].level | 3 |
| concepts[6].score | 0.06617787480354309 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q788926 |
| concepts[6].display_name | Infectious disease (medical specialty) |
| concepts[7].id | https://openalex.org/C116675565 |
| concepts[7].level | 2 |
| concepts[7].score | 0.03532245755195618 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q3241045 |
| concepts[7].display_name | Outbreak |
| concepts[8].id | https://openalex.org/C2779134260 |
| concepts[8].level | 2 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q12136 |
| concepts[8].display_name | Disease |
| keywords[0].id | https://openalex.org/keywords/coronavirus-disease-2019 |
| keywords[0].score | 0.8197652697563171 |
| keywords[0].display_name | Coronavirus disease 2019 (COVID-19) |
| keywords[1].id | https://openalex.org/keywords/virology |
| keywords[1].score | 0.6414763331413269 |
| keywords[1].display_name | Virology |
| keywords[2].id | https://openalex.org/keywords/severe-acute-respiratory-syndrome-coronavirus-2 |
| keywords[2].score | 0.5871832370758057 |
| keywords[2].display_name | Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) |
| keywords[3].id | https://openalex.org/keywords/2019-20-coronavirus-outbreak |
| keywords[3].score | 0.5819908976554871 |
| keywords[3].display_name | 2019-20 coronavirus outbreak |
| keywords[4].id | https://openalex.org/keywords/medicine |
| keywords[4].score | 0.4251735210418701 |
| keywords[4].display_name | Medicine |
| keywords[5].id | https://openalex.org/keywords/internal-medicine |
| keywords[5].score | 0.13189733028411865 |
| keywords[5].display_name | Internal medicine |
| keywords[6].id | https://openalex.org/keywords/infectious-disease |
| keywords[6].score | 0.06617787480354309 |
| keywords[6].display_name | Infectious disease (medical specialty) |
| keywords[7].id | https://openalex.org/keywords/outbreak |
| keywords[7].score | 0.03532245755195618 |
| keywords[7].display_name | Outbreak |
| language | en |
| locations[0].id | doi:10.1155/2022/8549707 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S170800122 |
| locations[0].source.issn | 1555-4309, 1555-4317 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1555-4309 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Contrast Media & Molecular Imaging |
| locations[0].source.host_organization | https://openalex.org/P4310319869 |
| locations[0].source.host_organization_name | Hindawi Publishing Corporation |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319869 |
| locations[0].source.host_organization_lineage_names | Hindawi Publishing Corporation |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://downloads.hindawi.com/journals/cmmi/2022/8549707.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Contrast Media & Molecular Imaging |
| locations[0].landing_page_url | https://doi.org/10.1155/2022/8549707 |
| locations[1].id | pmid:35280712 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Contrast media & molecular imaging |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/35280712 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:8884121 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | other-oa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/other-oa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Contrast Media Mol Imaging |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/8884121 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5051091627 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-9649-7757 |
| authorships[0].author.display_name | Muhammad Umar Aftab |
| authorships[0].countries | PK |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I173207729 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan |
| authorships[0].institutions[0].id | https://openalex.org/I173207729 |
| authorships[0].institutions[0].ror | https://ror.org/03v00ka07 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I173207729 |
| authorships[0].institutions[0].country_code | PK |
| authorships[0].institutions[0].display_name | University of Engineering and Technology Taxila |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Muhammad Aftab |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan |
| authorships[1].author.id | https://openalex.org/A5007077685 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-3143-689X |
| authorships[1].author.display_name | Rashid Amin |
| authorships[1].countries | PK |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I173207729 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan |
| authorships[1].institutions[0].id | https://openalex.org/I173207729 |
| authorships[1].institutions[0].ror | https://ror.org/03v00ka07 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I173207729 |
| authorships[1].institutions[0].country_code | PK |
| authorships[1].institutions[0].display_name | University of Engineering and Technology Taxila |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Rashid Amin |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan |
| authorships[2].author.id | https://openalex.org/A5009367259 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-1688-8772 |
| authorships[2].author.display_name | Deepika Koundal |
| authorships[2].countries | IN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I5847235 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Systemics, School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India |
| authorships[2].institutions[0].id | https://openalex.org/I5847235 |
| authorships[2].institutions[0].ror | https://ror.org/04q2jes40 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I5847235 |
| authorships[2].institutions[0].country_code | IN |
| authorships[2].institutions[0].display_name | University of Petroleum and Energy Studies |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Deepika Koundal |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Systemics, School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India |
| authorships[3].author.id | https://openalex.org/A5029317503 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-4141-0079 |
| authorships[3].author.display_name | Hamza Aldabbas |
| authorships[3].countries | JO |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I33926330 |
| authorships[3].affiliations[0].raw_affiliation_string | Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan |
| authorships[3].institutions[0].id | https://openalex.org/I33926330 |
| authorships[3].institutions[0].ror | https://ror.org/00qedmt22 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I33926330 |
| authorships[3].institutions[0].country_code | JO |
| authorships[3].institutions[0].display_name | Al-Balqa Applied University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Hamza Aldabbas |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan |
| authorships[4].author.id | https://openalex.org/A5000061018 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-7759-0940 |
| authorships[4].author.display_name | Bader Alouffi |
| authorships[4].countries | SA |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I179331831 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia |
| authorships[4].institutions[0].id | https://openalex.org/I179331831 |
| authorships[4].institutions[0].ror | https://ror.org/014g1a453 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I179331831 |
| authorships[4].institutions[0].country_code | SA |
| authorships[4].institutions[0].display_name | Taif University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Bader Alouffi |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia |
| authorships[5].author.id | https://openalex.org/A5034071040 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-4545-4092 |
| authorships[5].author.display_name | Zeshan Iqbal |
| authorships[5].countries | PK |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I173207729 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan |
| authorships[5].institutions[0].id | https://openalex.org/I173207729 |
| authorships[5].institutions[0].ror | https://ror.org/03v00ka07 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I173207729 |
| authorships[5].institutions[0].country_code | PK |
| authorships[5].institutions[0].display_name | University of Engineering and Technology Taxila |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Zeshan Iqbal |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://downloads.hindawi.com/journals/cmmi/2022/8549707.pdf |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Classification of COVID‐19 and Influenza Patients Using Deep Learning |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11775 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2741 |
| primary_topic.subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| primary_topic.display_name | COVID-19 diagnosis using AI |
| related_works | https://openalex.org/W3036314732, https://openalex.org/W3009669391, https://openalex.org/W3176864053, https://openalex.org/W4206669628, https://openalex.org/W3171943759, https://openalex.org/W4292098121, https://openalex.org/W3154141118, https://openalex.org/W4388896133, https://openalex.org/W3031607536, https://openalex.org/W4205317059 |
| cited_by_count | 17 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 4 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 6 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 5 |
| locations_count | 3 |
| best_oa_location.id | doi:10.1155/2022/8549707 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S170800122 |
| best_oa_location.source.issn | 1555-4309, 1555-4317 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 1555-4309 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Contrast Media & Molecular Imaging |
| best_oa_location.source.host_organization | https://openalex.org/P4310319869 |
| best_oa_location.source.host_organization_name | Hindawi Publishing Corporation |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319869 |
| best_oa_location.source.host_organization_lineage_names | Hindawi Publishing Corporation |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://downloads.hindawi.com/journals/cmmi/2022/8549707.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Contrast Media & Molecular Imaging |
| best_oa_location.landing_page_url | https://doi.org/10.1155/2022/8549707 |
| primary_location.id | doi:10.1155/2022/8549707 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S170800122 |
| primary_location.source.issn | 1555-4309, 1555-4317 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1555-4309 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Contrast Media & Molecular Imaging |
| primary_location.source.host_organization | https://openalex.org/P4310319869 |
| primary_location.source.host_organization_name | Hindawi Publishing Corporation |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319869 |
| primary_location.source.host_organization_lineage_names | Hindawi Publishing Corporation |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://downloads.hindawi.com/journals/cmmi/2022/8549707.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Contrast Media & Molecular Imaging |
| primary_location.landing_page_url | https://doi.org/10.1155/2022/8549707 |
| publication_date | 2022-01-01 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W3104019412, https://openalex.org/W3110228174, https://openalex.org/W3095700746, https://openalex.org/W3088635217, https://openalex.org/W3086808601, https://openalex.org/W3133191822, https://openalex.org/W3039985320, https://openalex.org/W2015403041, https://openalex.org/W2160881014, https://openalex.org/W4237959566, https://openalex.org/W4302435679, https://openalex.org/W2138064535, https://openalex.org/W3168156287, https://openalex.org/W3094265554, https://openalex.org/W3089168916, https://openalex.org/W3170900500, https://openalex.org/W3036552116, https://openalex.org/W1982646654, https://openalex.org/W3105180066, https://openalex.org/W3003920946, https://openalex.org/W3093777034, https://openalex.org/W3015049708, https://openalex.org/W3097511801, https://openalex.org/W3167147947, https://openalex.org/W3139338041, https://openalex.org/W3013896658, https://openalex.org/W3033689290, https://openalex.org/W3045910017, https://openalex.org/W2082036062, https://openalex.org/W2156172477, https://openalex.org/W3035936922, https://openalex.org/W3111722114, https://openalex.org/W3034277126, https://openalex.org/W3107766422, https://openalex.org/W3116978821, https://openalex.org/W2960671912, https://openalex.org/W2044097773, https://openalex.org/W3107475078, https://openalex.org/W2884821995, https://openalex.org/W2581684202 |
| referenced_works_count | 40 |
| abstract_inverted_index.A | 83 |
| abstract_inverted_index.a | 3, 63 |
| abstract_inverted_index.As | 29 |
| abstract_inverted_index.in | 14, 23, 37, 52, 122 |
| abstract_inverted_index.is | 2, 32, 44 |
| abstract_inverted_index.of | 27, 73, 87 |
| abstract_inverted_index.on | 126 |
| abstract_inverted_index.to | 35, 92, 97 |
| abstract_inverted_index.we | 77 |
| abstract_inverted_index.98% | 131 |
| abstract_inverted_index.CNN | 120 |
| abstract_inverted_index.For | 70 |
| abstract_inverted_index.Our | 111 |
| abstract_inverted_index.and | 16, 75, 108 |
| abstract_inverted_index.are | 56, 62 |
| abstract_inverted_index.can | 103 |
| abstract_inverted_index.for | 48, 66 |
| abstract_inverted_index.its | 38, 41, 53 |
| abstract_inverted_index.the | 20, 24, 50, 71, 119, 123 |
| abstract_inverted_index.was | 90 |
| abstract_inverted_index.Deep | 59 |
| abstract_inverted_index.deep | 80, 100 |
| abstract_inverted_index.long | 113 |
| abstract_inverted_index.test | 98 |
| abstract_inverted_index.that | 6 |
| abstract_inverted_index.this | 30 |
| abstract_inverted_index.tool | 65 |
| abstract_inverted_index.used | 91 |
| abstract_inverted_index.very | 33 |
| abstract_inverted_index.with | 9 |
| abstract_inverted_index.China | 15 |
| abstract_inverted_index.being | 57 |
| abstract_inverted_index.chest | 127 |
| abstract_inverted_index.early | 39, 54 |
| abstract_inverted_index.handy | 64 |
| abstract_inverted_index.model | 121 |
| abstract_inverted_index.phase | 125 |
| abstract_inverted_index.virus | 5, 31, 51 |
| abstract_inverted_index.(LSTM) | 116 |
| abstract_inverted_index.around | 19 |
| abstract_inverted_index.cases. | 110 |
| abstract_inverted_index.deadly | 4 |
| abstract_inverted_index.globe, | 21 |
| abstract_inverted_index.images | 89 |
| abstract_inverted_index.memory | 115 |
| abstract_inverted_index.models | 102 |
| abstract_inverted_index.spread | 18 |
| abstract_inverted_index.stages | 55 |
| abstract_inverted_index.starts | 8 |
| abstract_inverted_index.Several | 46 |
| abstract_inverted_index.X‐ray | 88, 128 |
| abstract_inverted_index.dataset | 86 |
| abstract_inverted_index.develop | 93 |
| abstract_inverted_index.emerged | 13 |
| abstract_inverted_index.images, | 129 |
| abstract_inverted_index.models. | 82, 95 |
| abstract_inverted_index.normal, | 106 |
| abstract_inverted_index.quickly | 17 |
| abstract_inverted_index.similar | 34 |
| abstract_inverted_index.stages, | 40 |
| abstract_inverted_index.various | 68 |
| abstract_inverted_index.accurate | 42 |
| abstract_inverted_index.diagnose | 105 |
| abstract_inverted_index.epidemic | 26 |
| abstract_inverted_index.learning | 60, 81, 101 |
| abstract_inverted_index.proposed | 78, 94, 112 |
| abstract_inverted_index.publicly | 84 |
| abstract_inverted_index.results, | 99 |
| abstract_inverted_index.tailored | 79 |
| abstract_inverted_index.According | 96 |
| abstract_inverted_index.accuracy. | 132 |
| abstract_inverted_index.achieving | 130 |
| abstract_inverted_index.available | 85 |
| abstract_inverted_index.detecting | 49, 67 |
| abstract_inverted_index.detection | 43 |
| abstract_inverted_index.diseases. | 69 |
| abstract_inverted_index.influenza | 36 |
| abstract_inverted_index.initially | 7 |
| abstract_inverted_index.resulting | 22 |
| abstract_inverted_index.symptoms. | 11 |
| abstract_inverted_index.technique | 117 |
| abstract_inverted_index.2019–22. | 28 |
| abstract_inverted_index.COVID‐19 | 12, 74, 109 |
| abstract_inverted_index.accurately | 104 |
| abstract_inverted_index.developed. | 58 |
| abstract_inverted_index.evaluation | 124 |
| abstract_inverted_index.flu‐like | 10 |
| abstract_inverted_index.influenza, | 76, 107 |
| abstract_inverted_index.techniques | 47, 61 |
| abstract_inverted_index.Coronavirus | 0 |
| abstract_inverted_index.coronavirus | 25 |
| abstract_inverted_index.(COVID‐19) | 1 |
| abstract_inverted_index.challenging. | 45 |
| abstract_inverted_index.outperformed | 118 |
| abstract_inverted_index.short‐term | 114 |
| abstract_inverted_index.classification | 72 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 95 |
| corresponding_author_ids | https://openalex.org/A5007077685 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I173207729 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8700000047683716 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.89277914 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |