Ftl‐CoV19: A Transfer Learning Approach to Detect COVID‐19 Article Swipe
Tarishi Singh
,
Praneet Saurabh
,
Dhananjay Bisen
,
Lalit Kane
,
Mayank Pathak
,
G. R. Sinha
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/1953992
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/1953992
COVID‐19 is an infectious and contagious disease caused by the new coronavirus. The total number of cases is over 19 million and continues to grow. A common symptom noticed among COVID‐19 patients is lung infection that results in breathlessness, and the lack of essential resources such as testing, oxygen, and ventilators enhances its severity. Chest X‐ray can be used to design and develop a COVID‐19 detection mechanism for a quicker diagnosis using AI and machine learning techniques. Due to this silver lining, various new COVID‐19 detection techniques and prediction models have been introduced in recent times based on chest radiography images. However, due to a high level of unpredictability and the absence of essential data, standard models have showcased low efficiency and also suffer from overheads and complexities. This paper proposes a model fine tuning transfer learning‐coronavirus 19 (Ftl‐CoV19) for COVID‐19 detection through chest X‐rays, which embraces the ideas of transfer learning in pretrained VGG16 model with including combination of convolution, max pooling, and dense layer at different stages of model. Ftl‐CoV19 reported promising experimental results; it observed training and validation accuracy of 98.82% and 99.27% with precision of 100%, recall of 98%, and F1 score of 99%. These results outperformed other conventional state of arts such as CNN, ResNet50, InceptionV3, and Xception.
Related Topics
Concepts
Transfer of learning
Coronavirus disease 2019 (COVID-19)
Computer science
Pooling
Artificial intelligence
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Recall
Machine learning
2019-20 coronavirus outbreak
Training set
Convolutional neural network
Medicine
Disease
Pathology
Infectious disease (medical specialty)
Outbreak
Philosophy
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/1953992
- https://downloads.hindawi.com/journals/cin/2022/1953992.pdf
- OA Status
- hybrid
- Cited By
- 6
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4286634485
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4286634485Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2022/1953992Digital Object Identifier
- Title
-
Ftl‐CoV19: A Transfer Learning Approach to Detect COVID‐19Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Tarishi Singh, Praneet Saurabh, Dhananjay Bisen, Lalit Kane, Mayank Pathak, G. R. SinhaList of authors in order
- Landing page
-
https://doi.org/10.1155/2022/1953992Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/cin/2022/1953992.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/cin/2022/1953992.pdfDirect OA link when available
- Concepts
-
Transfer of learning, Coronavirus disease 2019 (COVID-19), Computer science, Pooling, Artificial intelligence, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Recall, Machine learning, 2019-20 coronavirus outbreak, Training set, Convolutional neural network, Medicine, Disease, Pathology, Infectious disease (medical specialty), Outbreak, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 3, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
28Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4286634485 |
|---|---|
| doi | https://doi.org/10.1155/2022/1953992 |
| ids.doi | https://doi.org/10.1155/2022/1953992 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/35865493 |
| ids.openalex | https://openalex.org/W4286634485 |
| fwci | 1.17149707 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D000465 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Algorithms |
| mesh[1].qualifier_ui | Q000175 |
| mesh[1].descriptor_ui | D000086382 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | diagnosis |
| 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 | D006801 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Humans |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D016571 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Neural Networks, Computer |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D000086402 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | SARS-CoV-2 |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D000465 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Algorithms |
| mesh[7].qualifier_ui | Q000175 |
| mesh[7].descriptor_ui | D000086382 |
| mesh[7].is_major_topic | True |
| mesh[7].qualifier_name | diagnosis |
| mesh[7].descriptor_name | COVID-19 |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D000077321 |
| mesh[8].is_major_topic | True |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Deep Learning |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D006801 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Humans |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D016571 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Neural Networks, Computer |
| mesh[11].qualifier_ui | |
| mesh[11].descriptor_ui | D000086402 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | |
| mesh[11].descriptor_name | SARS-CoV-2 |
| mesh[12].qualifier_ui | |
| mesh[12].descriptor_ui | D000465 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | |
| mesh[12].descriptor_name | Algorithms |
| mesh[13].qualifier_ui | Q000175 |
| mesh[13].descriptor_ui | D000086382 |
| mesh[13].is_major_topic | True |
| mesh[13].qualifier_name | diagnosis |
| mesh[13].descriptor_name | COVID-19 |
| mesh[14].qualifier_ui | |
| mesh[14].descriptor_ui | D000077321 |
| mesh[14].is_major_topic | True |
| mesh[14].qualifier_name | |
| mesh[14].descriptor_name | Deep Learning |
| mesh[15].qualifier_ui | |
| mesh[15].descriptor_ui | D006801 |
| mesh[15].is_major_topic | False |
| mesh[15].qualifier_name | |
| mesh[15].descriptor_name | Humans |
| mesh[16].qualifier_ui | |
| mesh[16].descriptor_ui | D016571 |
| mesh[16].is_major_topic | False |
| mesh[16].qualifier_name | |
| mesh[16].descriptor_name | Neural Networks, Computer |
| mesh[17].qualifier_ui | |
| mesh[17].descriptor_ui | D000086402 |
| mesh[17].is_major_topic | False |
| mesh[17].qualifier_name | |
| mesh[17].descriptor_name | SARS-CoV-2 |
| mesh[18].qualifier_ui | |
| mesh[18].descriptor_ui | D000465 |
| mesh[18].is_major_topic | False |
| mesh[18].qualifier_name | |
| mesh[18].descriptor_name | Algorithms |
| mesh[19].qualifier_ui | Q000175 |
| mesh[19].descriptor_ui | D000086382 |
| mesh[19].is_major_topic | True |
| mesh[19].qualifier_name | diagnosis |
| mesh[19].descriptor_name | COVID-19 |
| mesh[20].qualifier_ui | |
| mesh[20].descriptor_ui | D000077321 |
| mesh[20].is_major_topic | True |
| mesh[20].qualifier_name | |
| mesh[20].descriptor_name | Deep Learning |
| mesh[21].qualifier_ui | |
| mesh[21].descriptor_ui | D006801 |
| mesh[21].is_major_topic | False |
| mesh[21].qualifier_name | |
| mesh[21].descriptor_name | Humans |
| mesh[22].qualifier_ui | |
| mesh[22].descriptor_ui | D016571 |
| mesh[22].is_major_topic | False |
| mesh[22].qualifier_name | |
| mesh[22].descriptor_name | Neural Networks, Computer |
| mesh[23].qualifier_ui | |
| mesh[23].descriptor_ui | D000086402 |
| mesh[23].is_major_topic | False |
| mesh[23].qualifier_name | |
| mesh[23].descriptor_name | SARS-CoV-2 |
| mesh[24].qualifier_ui | |
| mesh[24].descriptor_ui | D000465 |
| mesh[24].is_major_topic | False |
| mesh[24].qualifier_name | |
| mesh[24].descriptor_name | Algorithms |
| mesh[25].qualifier_ui | Q000175 |
| mesh[25].descriptor_ui | D000086382 |
| mesh[25].is_major_topic | True |
| mesh[25].qualifier_name | diagnosis |
| mesh[25].descriptor_name | COVID-19 |
| mesh[26].qualifier_ui | |
| mesh[26].descriptor_ui | D000077321 |
| mesh[26].is_major_topic | True |
| mesh[26].qualifier_name | |
| mesh[26].descriptor_name | Deep Learning |
| mesh[27].qualifier_ui | |
| mesh[27].descriptor_ui | D006801 |
| mesh[27].is_major_topic | False |
| mesh[27].qualifier_name | |
| mesh[27].descriptor_name | Humans |
| mesh[28].qualifier_ui | |
| mesh[28].descriptor_ui | D016571 |
| mesh[28].is_major_topic | False |
| mesh[28].qualifier_name | |
| mesh[28].descriptor_name | Neural Networks, Computer |
| mesh[29].qualifier_ui | |
| mesh[29].descriptor_ui | D000086402 |
| mesh[29].is_major_topic | False |
| mesh[29].qualifier_name | |
| mesh[29].descriptor_name | SARS-CoV-2 |
| mesh[30].qualifier_ui | |
| mesh[30].descriptor_ui | D000465 |
| mesh[30].is_major_topic | False |
| mesh[30].qualifier_name | |
| mesh[30].descriptor_name | Algorithms |
| mesh[31].qualifier_ui | Q000175 |
| mesh[31].descriptor_ui | D000086382 |
| mesh[31].is_major_topic | True |
| mesh[31].qualifier_name | diagnosis |
| mesh[31].descriptor_name | COVID-19 |
| mesh[32].qualifier_ui | |
| mesh[32].descriptor_ui | D000077321 |
| mesh[32].is_major_topic | True |
| mesh[32].qualifier_name | |
| mesh[32].descriptor_name | Deep Learning |
| mesh[33].qualifier_ui | |
| mesh[33].descriptor_ui | D006801 |
| mesh[33].is_major_topic | False |
| mesh[33].qualifier_name | |
| mesh[33].descriptor_name | Humans |
| mesh[34].qualifier_ui | |
| mesh[34].descriptor_ui | D016571 |
| mesh[34].is_major_topic | False |
| mesh[34].qualifier_name | |
| mesh[34].descriptor_name | Neural Networks, Computer |
| mesh[35].qualifier_ui | |
| mesh[35].descriptor_ui | D000086402 |
| mesh[35].is_major_topic | False |
| mesh[35].qualifier_name | |
| mesh[35].descriptor_name | SARS-CoV-2 |
| mesh[36].qualifier_ui | |
| mesh[36].descriptor_ui | D000465 |
| mesh[36].is_major_topic | False |
| mesh[36].qualifier_name | |
| mesh[36].descriptor_name | Algorithms |
| mesh[37].qualifier_ui | Q000175 |
| mesh[37].descriptor_ui | D000086382 |
| mesh[37].is_major_topic | True |
| mesh[37].qualifier_name | diagnosis |
| mesh[37].descriptor_name | COVID-19 |
| mesh[38].qualifier_ui | |
| mesh[38].descriptor_ui | D000077321 |
| mesh[38].is_major_topic | True |
| mesh[38].qualifier_name | |
| mesh[38].descriptor_name | Deep Learning |
| mesh[39].qualifier_ui | |
| mesh[39].descriptor_ui | D006801 |
| mesh[39].is_major_topic | False |
| mesh[39].qualifier_name | |
| mesh[39].descriptor_name | Humans |
| mesh[40].qualifier_ui | |
| mesh[40].descriptor_ui | D016571 |
| mesh[40].is_major_topic | False |
| mesh[40].qualifier_name | |
| mesh[40].descriptor_name | Neural Networks, Computer |
| mesh[41].qualifier_ui | |
| mesh[41].descriptor_ui | D000086402 |
| mesh[41].is_major_topic | False |
| mesh[41].qualifier_name | |
| mesh[41].descriptor_name | SARS-CoV-2 |
| type | article |
| title | Ftl‐CoV19: A Transfer Learning Approach to Detect COVID‐19 |
| biblio.issue | 1 |
| biblio.volume | 2022 |
| biblio.last_page | 1953992 |
| biblio.first_page | 1953992 |
| 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/T13018 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9783999919891357 |
| 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 | Seismology and Earthquake Studies |
| topics[2].id | https://openalex.org/T12424 |
| topics[2].field.id | https://openalex.org/fields/19 |
| topics[2].field.display_name | Earth and Planetary Sciences |
| topics[2].score | 0.9692999720573425 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1908 |
| topics[2].subfield.display_name | Geophysics |
| topics[2].display_name | Earthquake Detection and Analysis |
| is_xpac | False |
| apc_list.value | 2100 |
| apc_list.currency | USD |
| apc_list.value_usd | 2100 |
| apc_paid.value | 2100 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 2100 |
| concepts[0].id | https://openalex.org/C150899416 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8324019908905029 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1820378 |
| concepts[0].display_name | Transfer of learning |
| concepts[1].id | https://openalex.org/C3008058167 |
| concepts[1].level | 4 |
| concepts[1].score | 0.8279039859771729 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q84263196 |
| concepts[1].display_name | Coronavirus disease 2019 (COVID-19) |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.74867844581604 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C70437156 |
| concepts[3].level | 2 |
| concepts[3].score | 0.7386580109596252 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q7228652 |
| concepts[3].display_name | Pooling |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5925655364990234 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C3007834351 |
| concepts[5].level | 5 |
| concepts[5].score | 0.5793955326080322 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q82069695 |
| concepts[5].display_name | Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) |
| concepts[6].id | https://openalex.org/C100660578 |
| concepts[6].level | 2 |
| concepts[6].score | 0.46763256192207336 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q18733 |
| concepts[6].display_name | Recall |
| concepts[7].id | https://openalex.org/C119857082 |
| concepts[7].level | 1 |
| concepts[7].score | 0.4619036316871643 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[7].display_name | Machine learning |
| concepts[8].id | https://openalex.org/C3006700255 |
| concepts[8].level | 3 |
| concepts[8].score | 0.4503431022167206 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q81068910 |
| concepts[8].display_name | 2019-20 coronavirus outbreak |
| concepts[9].id | https://openalex.org/C51632099 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4466024339199066 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q3985153 |
| concepts[9].display_name | Training set |
| concepts[10].id | https://openalex.org/C81363708 |
| concepts[10].level | 2 |
| concepts[10].score | 0.42374303936958313 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[10].display_name | Convolutional neural network |
| concepts[11].id | https://openalex.org/C71924100 |
| concepts[11].level | 0 |
| concepts[11].score | 0.18854695558547974 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[11].display_name | Medicine |
| concepts[12].id | https://openalex.org/C2779134260 |
| concepts[12].level | 2 |
| concepts[12].score | 0.17602801322937012 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q12136 |
| concepts[12].display_name | Disease |
| concepts[13].id | https://openalex.org/C142724271 |
| concepts[13].level | 1 |
| concepts[13].score | 0.10967540740966797 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q7208 |
| concepts[13].display_name | Pathology |
| concepts[14].id | https://openalex.org/C524204448 |
| concepts[14].level | 3 |
| concepts[14].score | 0.08985301852226257 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q788926 |
| concepts[14].display_name | Infectious disease (medical specialty) |
| concepts[15].id | https://openalex.org/C116675565 |
| concepts[15].level | 2 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q3241045 |
| concepts[15].display_name | Outbreak |
| concepts[16].id | https://openalex.org/C138885662 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[16].display_name | Philosophy |
| concepts[17].id | https://openalex.org/C41895202 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[17].display_name | Linguistics |
| keywords[0].id | https://openalex.org/keywords/transfer-of-learning |
| keywords[0].score | 0.8324019908905029 |
| keywords[0].display_name | Transfer of learning |
| keywords[1].id | https://openalex.org/keywords/coronavirus-disease-2019 |
| keywords[1].score | 0.8279039859771729 |
| keywords[1].display_name | Coronavirus disease 2019 (COVID-19) |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.74867844581604 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/pooling |
| keywords[3].score | 0.7386580109596252 |
| keywords[3].display_name | Pooling |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.5925655364990234 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/severe-acute-respiratory-syndrome-coronavirus-2 |
| keywords[5].score | 0.5793955326080322 |
| keywords[5].display_name | Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) |
| keywords[6].id | https://openalex.org/keywords/recall |
| keywords[6].score | 0.46763256192207336 |
| keywords[6].display_name | Recall |
| keywords[7].id | https://openalex.org/keywords/machine-learning |
| keywords[7].score | 0.4619036316871643 |
| keywords[7].display_name | Machine learning |
| keywords[8].id | https://openalex.org/keywords/2019-20-coronavirus-outbreak |
| keywords[8].score | 0.4503431022167206 |
| keywords[8].display_name | 2019-20 coronavirus outbreak |
| keywords[9].id | https://openalex.org/keywords/training-set |
| keywords[9].score | 0.4466024339199066 |
| keywords[9].display_name | Training set |
| keywords[10].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[10].score | 0.42374303936958313 |
| keywords[10].display_name | Convolutional neural network |
| keywords[11].id | https://openalex.org/keywords/medicine |
| keywords[11].score | 0.18854695558547974 |
| keywords[11].display_name | Medicine |
| keywords[12].id | https://openalex.org/keywords/disease |
| keywords[12].score | 0.17602801322937012 |
| keywords[12].display_name | Disease |
| keywords[13].id | https://openalex.org/keywords/pathology |
| keywords[13].score | 0.10967540740966797 |
| keywords[13].display_name | Pathology |
| keywords[14].id | https://openalex.org/keywords/infectious-disease |
| keywords[14].score | 0.08985301852226257 |
| keywords[14].display_name | Infectious disease (medical specialty) |
| language | en |
| locations[0].id | doi:10.1155/2022/1953992 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S72372694 |
| locations[0].source.issn | 1687-5265, 1687-5273 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1687-5265 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Computational Intelligence and Neuroscience |
| 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/cin/2022/1953992.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 | Computational Intelligence and Neuroscience |
| locations[0].landing_page_url | https://doi.org/10.1155/2022/1953992 |
| locations[1].id | pmid:35865493 |
| 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 | Computational intelligence and neuroscience |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/35865493 |
| locations[2].id | pmh:oai:doaj.org/article:70479e8b5aba43b5839d28afd1d82e4d |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| 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 | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Computational Intelligence and Neuroscience, Vol 2022 (2022) |
| locations[2].landing_page_url | https://doaj.org/article/70479e8b5aba43b5839d28afd1d82e4d |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:9295614 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Comput Intell Neurosci |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/9295614 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5063684411 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-7619-1988 |
| authorships[0].author.display_name | Tarishi Singh |
| authorships[0].countries | IN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I72563045 |
| authorships[0].affiliations[0].raw_affiliation_string | Mody University of Science and Technology, Lachhmangarh, Rajasthan, India |
| authorships[0].institutions[0].id | https://openalex.org/I72563045 |
| authorships[0].institutions[0].ror | https://ror.org/00kxafy15 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I72563045 |
| authorships[0].institutions[0].country_code | IN |
| authorships[0].institutions[0].display_name | Mody University of Science and Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Tarishi Singh |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Mody University of Science and Technology, Lachhmangarh, Rajasthan, India |
| authorships[1].author.id | https://openalex.org/A5046363887 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-3782-4279 |
| authorships[1].author.display_name | Praneet Saurabh |
| authorships[1].countries | IN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I72563045 |
| authorships[1].affiliations[0].raw_affiliation_string | Mody University of Science and Technology, Lachhmangarh, Rajasthan, India |
| authorships[1].institutions[0].id | https://openalex.org/I72563045 |
| authorships[1].institutions[0].ror | https://ror.org/00kxafy15 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I72563045 |
| authorships[1].institutions[0].country_code | IN |
| authorships[1].institutions[0].display_name | Mody University of Science and Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Praneet Saurabh |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Mody University of Science and Technology, Lachhmangarh, Rajasthan, India |
| authorships[2].author.id | https://openalex.org/A5012816906 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-4165-3959 |
| authorships[2].author.display_name | Dhananjay Bisen |
| authorships[2].countries | IN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I9747756 |
| authorships[2].affiliations[0].raw_affiliation_string | Madhav Institute of Technology and Sciences, Gwalior, Madhya Pradesh, India |
| authorships[2].institutions[0].id | https://openalex.org/I9747756 |
| authorships[2].institutions[0].ror | https://ror.org/008b3ap06 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I9747756 |
| authorships[2].institutions[0].country_code | IN |
| authorships[2].institutions[0].display_name | Atal Bihari Vajpayee Indian Institute of Information Technology and Management |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Dhananjay Bisen |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Madhav Institute of Technology and Sciences, Gwalior, Madhya Pradesh, India |
| authorships[3].author.id | https://openalex.org/A5089300960 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-7305-1189 |
| authorships[3].author.display_name | Lalit Kane |
| authorships[3].countries | IN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I5847235 |
| authorships[3].affiliations[0].raw_affiliation_string | University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India |
| authorships[3].institutions[0].id | https://openalex.org/I5847235 |
| authorships[3].institutions[0].ror | https://ror.org/04q2jes40 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I5847235 |
| authorships[3].institutions[0].country_code | IN |
| authorships[3].institutions[0].display_name | University of Petroleum and Energy Studies |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Lalit Kane |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India |
| authorships[4].author.id | https://openalex.org/A5080065449 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-3544-9775 |
| authorships[4].author.display_name | Mayank Pathak |
| authorships[4].affiliations[0].raw_affiliation_string | Technocrats Institute of Technology, Bhopal, Madhya Pradesh, India |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Mayank Pathak |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Technocrats Institute of Technology, Bhopal, Madhya Pradesh, India |
| authorships[5].author.id | https://openalex.org/A5085796013 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-2384-4591 |
| authorships[5].author.display_name | G. R. Sinha |
| authorships[5].countries | MM |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I3132516330 |
| authorships[5].affiliations[0].raw_affiliation_string | Myanmar Institute of Information Technology, Mandalay, Myanmar |
| authorships[5].institutions[0].id | https://openalex.org/I3132516330 |
| authorships[5].institutions[0].ror | https://ror.org/038smxt70 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I3132516330 |
| authorships[5].institutions[0].country_code | MM |
| authorships[5].institutions[0].display_name | Myanmar Institute of Information Technology |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | G. R. Sinha |
| authorships[5].is_corresponding | True |
| authorships[5].raw_affiliation_strings | Myanmar Institute of Information Technology, Mandalay, Myanmar |
| 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/cin/2022/1953992.pdf |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Ftl‐CoV19: A Transfer Learning Approach to Detect COVID‐19 |
| 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/W2953234277, https://openalex.org/W2626256601, https://openalex.org/W2810679507, https://openalex.org/W3183901164, https://openalex.org/W3135818718, https://openalex.org/W4290188444, https://openalex.org/W3167935049, https://openalex.org/W3003905048, https://openalex.org/W2253429366, https://openalex.org/W3127975138 |
| cited_by_count | 6 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 3 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 1 |
| locations_count | 4 |
| best_oa_location.id | doi:10.1155/2022/1953992 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S72372694 |
| best_oa_location.source.issn | 1687-5265, 1687-5273 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 1687-5265 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Computational Intelligence and Neuroscience |
| 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/cin/2022/1953992.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 | Computational Intelligence and Neuroscience |
| best_oa_location.landing_page_url | https://doi.org/10.1155/2022/1953992 |
| primary_location.id | doi:10.1155/2022/1953992 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S72372694 |
| primary_location.source.issn | 1687-5265, 1687-5273 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1687-5265 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Computational Intelligence and Neuroscience |
| 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/cin/2022/1953992.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 | Computational Intelligence and Neuroscience |
| primary_location.landing_page_url | https://doi.org/10.1155/2022/1953992 |
| publication_date | 2022-01-01 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W4206091727, https://openalex.org/W3035697911, https://openalex.org/W3161094615, https://openalex.org/W3027914507, https://openalex.org/W3012867892, https://openalex.org/W3030419021, https://openalex.org/W3014725478, https://openalex.org/W3126359513, https://openalex.org/W2990346675, https://openalex.org/W3046481704, https://openalex.org/W3088122790, https://openalex.org/W3092974990, https://openalex.org/W3049688808, https://openalex.org/W3019283372, https://openalex.org/W3011414569, https://openalex.org/W3023402713, https://openalex.org/W3028070348, https://openalex.org/W2792245367, https://openalex.org/W3017141460, https://openalex.org/W2979617155, https://openalex.org/W3144030700, https://openalex.org/W3028427008, https://openalex.org/W2962835968, https://openalex.org/W2965127303, https://openalex.org/W3093238181, https://openalex.org/W3159001838, https://openalex.org/W3104810384, https://openalex.org/W4312066404 |
| referenced_works_count | 28 |
| abstract_inverted_index.A | 25 |
| abstract_inverted_index.a | 63, 68, 104, 131 |
| abstract_inverted_index.19 | 19, 137 |
| abstract_inverted_index.AI | 72 |
| abstract_inverted_index.F1 | 194 |
| abstract_inverted_index.an | 2 |
| abstract_inverted_index.as | 46, 207 |
| abstract_inverted_index.at | 166 |
| abstract_inverted_index.be | 57 |
| abstract_inverted_index.by | 8 |
| abstract_inverted_index.in | 37, 93, 152 |
| abstract_inverted_index.is | 1, 17, 32 |
| abstract_inverted_index.it | 176 |
| abstract_inverted_index.of | 15, 42, 107, 112, 149, 159, 169, 182, 188, 191, 196, 204 |
| abstract_inverted_index.on | 97 |
| abstract_inverted_index.to | 23, 59, 78, 103 |
| abstract_inverted_index.Due | 77 |
| abstract_inverted_index.The | 12 |
| abstract_inverted_index.and | 4, 21, 39, 49, 61, 73, 87, 109, 121, 126, 163, 179, 184, 193, 211 |
| abstract_inverted_index.can | 56 |
| abstract_inverted_index.due | 102 |
| abstract_inverted_index.for | 67, 139 |
| abstract_inverted_index.its | 52 |
| abstract_inverted_index.low | 119 |
| abstract_inverted_index.max | 161 |
| abstract_inverted_index.new | 10, 83 |
| abstract_inverted_index.the | 9, 40, 110, 147 |
| abstract_inverted_index.98%, | 192 |
| abstract_inverted_index.99%. | 197 |
| abstract_inverted_index.CNN, | 208 |
| abstract_inverted_index.This | 128 |
| abstract_inverted_index.also | 122 |
| abstract_inverted_index.arts | 205 |
| abstract_inverted_index.been | 91 |
| abstract_inverted_index.fine | 133 |
| abstract_inverted_index.from | 124 |
| abstract_inverted_index.have | 90, 117 |
| abstract_inverted_index.high | 105 |
| abstract_inverted_index.lack | 41 |
| abstract_inverted_index.lung | 33 |
| abstract_inverted_index.over | 18 |
| abstract_inverted_index.such | 45, 206 |
| abstract_inverted_index.that | 35 |
| abstract_inverted_index.this | 79 |
| abstract_inverted_index.used | 58 |
| abstract_inverted_index.with | 156, 186 |
| abstract_inverted_index.100%, | 189 |
| abstract_inverted_index.Chest | 54 |
| abstract_inverted_index.These | 198 |
| abstract_inverted_index.VGG16 | 154 |
| abstract_inverted_index.among | 29 |
| abstract_inverted_index.based | 96 |
| abstract_inverted_index.cases | 16 |
| abstract_inverted_index.chest | 98, 143 |
| abstract_inverted_index.data, | 114 |
| abstract_inverted_index.dense | 164 |
| abstract_inverted_index.grow. | 24 |
| abstract_inverted_index.ideas | 148 |
| abstract_inverted_index.layer | 165 |
| abstract_inverted_index.level | 106 |
| abstract_inverted_index.model | 132, 155 |
| abstract_inverted_index.other | 201 |
| abstract_inverted_index.paper | 129 |
| abstract_inverted_index.score | 195 |
| abstract_inverted_index.state | 203 |
| abstract_inverted_index.times | 95 |
| abstract_inverted_index.total | 13 |
| abstract_inverted_index.using | 71 |
| abstract_inverted_index.which | 145 |
| abstract_inverted_index.98.82% | 183 |
| abstract_inverted_index.99.27% | 185 |
| abstract_inverted_index.caused | 7 |
| abstract_inverted_index.common | 26 |
| abstract_inverted_index.design | 60 |
| abstract_inverted_index.model. | 170 |
| abstract_inverted_index.models | 89, 116 |
| abstract_inverted_index.number | 14 |
| abstract_inverted_index.recall | 190 |
| abstract_inverted_index.recent | 94 |
| abstract_inverted_index.silver | 80 |
| abstract_inverted_index.stages | 168 |
| abstract_inverted_index.suffer | 123 |
| abstract_inverted_index.tuning | 134 |
| abstract_inverted_index.X‐ray | 55 |
| abstract_inverted_index.absence | 111 |
| abstract_inverted_index.develop | 62 |
| abstract_inverted_index.disease | 6 |
| abstract_inverted_index.images. | 100 |
| abstract_inverted_index.lining, | 81 |
| abstract_inverted_index.machine | 74 |
| abstract_inverted_index.million | 20 |
| abstract_inverted_index.noticed | 28 |
| abstract_inverted_index.oxygen, | 48 |
| abstract_inverted_index.quicker | 69 |
| abstract_inverted_index.results | 36, 199 |
| abstract_inverted_index.symptom | 27 |
| abstract_inverted_index.through | 142 |
| abstract_inverted_index.various | 82 |
| abstract_inverted_index.However, | 101 |
| abstract_inverted_index.accuracy | 181 |
| abstract_inverted_index.embraces | 146 |
| abstract_inverted_index.enhances | 51 |
| abstract_inverted_index.learning | 75, 151 |
| abstract_inverted_index.observed | 177 |
| abstract_inverted_index.patients | 31 |
| abstract_inverted_index.pooling, | 162 |
| abstract_inverted_index.proposes | 130 |
| abstract_inverted_index.reported | 172 |
| abstract_inverted_index.results; | 175 |
| abstract_inverted_index.standard | 115 |
| abstract_inverted_index.testing, | 47 |
| abstract_inverted_index.training | 178 |
| abstract_inverted_index.transfer | 135, 150 |
| abstract_inverted_index.ResNet50, | 209 |
| abstract_inverted_index.Xception. | 212 |
| abstract_inverted_index.X‐rays, | 144 |
| abstract_inverted_index.continues | 22 |
| abstract_inverted_index.detection | 65, 85, 141 |
| abstract_inverted_index.diagnosis | 70 |
| abstract_inverted_index.different | 167 |
| abstract_inverted_index.essential | 43, 113 |
| abstract_inverted_index.including | 157 |
| abstract_inverted_index.infection | 34 |
| abstract_inverted_index.mechanism | 66 |
| abstract_inverted_index.overheads | 125 |
| abstract_inverted_index.precision | 187 |
| abstract_inverted_index.promising | 173 |
| abstract_inverted_index.resources | 44 |
| abstract_inverted_index.severity. | 53 |
| abstract_inverted_index.showcased | 118 |
| abstract_inverted_index.COVID‐19 | 0, 30, 64, 84, 140 |
| abstract_inverted_index.contagious | 5 |
| abstract_inverted_index.efficiency | 120 |
| abstract_inverted_index.infectious | 3 |
| abstract_inverted_index.introduced | 92 |
| abstract_inverted_index.prediction | 88 |
| abstract_inverted_index.pretrained | 153 |
| abstract_inverted_index.techniques | 86 |
| abstract_inverted_index.validation | 180 |
| abstract_inverted_index.Ftl‐CoV19 | 171 |
| abstract_inverted_index.combination | 158 |
| abstract_inverted_index.radiography | 99 |
| abstract_inverted_index.techniques. | 76 |
| abstract_inverted_index.ventilators | 50 |
| abstract_inverted_index.InceptionV3, | 210 |
| abstract_inverted_index.conventional | 202 |
| abstract_inverted_index.convolution, | 160 |
| abstract_inverted_index.coronavirus. | 11 |
| abstract_inverted_index.experimental | 174 |
| abstract_inverted_index.outperformed | 200 |
| abstract_inverted_index.(Ftl‐CoV19) | 138 |
| abstract_inverted_index.complexities. | 127 |
| abstract_inverted_index.breathlessness, | 38 |
| abstract_inverted_index.unpredictability | 108 |
| abstract_inverted_index.learning‐coronavirus | 136 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5085796013 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I3132516330 |
| citation_normalized_percentile.value | 0.7281874 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |