Revolutionizing anemia detection: integrative machine learning models and advanced attention mechanisms Article Swipe
Muhammad Ramzan
,
Jinfang Sheng
,
Muhammad Usman Saeed
,
Bin Wang
,
Faisal Z. Duraihem
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s42492-024-00169-4
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s42492-024-00169-4
This study addresses the critical issue of anemia detection using machine learning (ML) techniques. Although a widespread blood disorder with significant health implications, anemia often remains undetected. This necessitates timely and efficient diagnostic methods, as traditional approaches that rely on manual assessment are time-consuming and subjective. The present study explored the application of ML – particularly classification models, such as logistic regression, decision trees, random forest, support vector machines, Naïve Bayes, and k-nearest neighbors – in conjunction with innovative models incorporating attention modules and spatial attention to detect anemia. The proposed models demonstrated promising results, achieving high accuracy, precision, recall, and F1 scores for both textual and image datasets. In addition, an integrated approach that combines textual and image data was found to outperform the individual modalities. Specifically, the proposed AlexNet Multiple Spatial Attention model achieved an exceptional accuracy of 99.58%, emphasizing its potential to revolutionize automated anemia detection. The results of ablation studies confirm the significance of key components – including the blue-green-red, multiple, and spatial attentions – in enhancing model performance. Overall, this study presents a comprehensive and innovative framework for noninvasive anemia detection, contributing valuable insights to the field.
Related Topics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s42492-024-00169-4
- OA Status
- diamond
- Cited By
- 20
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400741984
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4400741984Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1186/s42492-024-00169-4Digital Object Identifier
- Title
-
Revolutionizing anemia detection: integrative machine learning models and advanced attention mechanismsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-17Full publication date if available
- Authors
-
Muhammad Ramzan, Jinfang Sheng, Muhammad Usman Saeed, Bin Wang, Faisal Z. DuraihemList of authors in order
- Landing page
-
https://doi.org/10.1186/s42492-024-00169-4Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1186/s42492-024-00169-4Direct OA link when available
- Concepts
-
Computer science, Cognitive science, Artificial intelligence, Machine learning, PsychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
20Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 15, 2024: 5Per-year citation counts (last 5 years)
- References (count)
-
36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4400741984 |
|---|---|
| doi | https://doi.org/10.1186/s42492-024-00169-4 |
| ids.doi | https://doi.org/10.1186/s42492-024-00169-4 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/39017765 |
| ids.openalex | https://openalex.org/W4400741984 |
| fwci | 12.77557098 |
| type | article |
| title | Revolutionizing anemia detection: integrative machine learning models and advanced attention mechanisms |
| biblio.issue | 1 |
| biblio.volume | 7 |
| biblio.last_page | 18 |
| biblio.first_page | 18 |
| topics[0].id | https://openalex.org/T13702 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9994999766349792 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Machine Learning in Healthcare |
| topics[1].id | https://openalex.org/T11396 |
| topics[1].field.id | https://openalex.org/fields/36 |
| topics[1].field.display_name | Health Professions |
| topics[1].score | 0.9873999953269958 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/3605 |
| topics[1].subfield.display_name | Health Information Management |
| topics[1].display_name | Artificial Intelligence in Healthcare |
| topics[2].id | https://openalex.org/T10028 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9710999727249146 |
| 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 | Topic Modeling |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.48083505034446716 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C188147891 |
| concepts[1].level | 1 |
| concepts[1].score | 0.437602162361145 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q147638 |
| concepts[1].display_name | Cognitive science |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.38802340626716614 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C119857082 |
| concepts[3].level | 1 |
| concepts[3].score | 0.32193511724472046 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[3].display_name | Machine learning |
| concepts[4].id | https://openalex.org/C15744967 |
| concepts[4].level | 0 |
| concepts[4].score | 0.31978839635849 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[4].display_name | Psychology |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.48083505034446716 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/cognitive-science |
| keywords[1].score | 0.437602162361145 |
| keywords[1].display_name | Cognitive science |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.38802340626716614 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/machine-learning |
| keywords[3].score | 0.32193511724472046 |
| keywords[3].display_name | Machine learning |
| keywords[4].id | https://openalex.org/keywords/psychology |
| keywords[4].score | 0.31978839635849 |
| keywords[4].display_name | Psychology |
| language | en |
| locations[0].id | doi:10.1186/s42492-024-00169-4 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S3035464634 |
| locations[0].source.issn | 2096-496X, 2524-4442 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2096-496X |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Visual Computing for Industry Biomedicine and Art |
| locations[0].source.host_organization | https://openalex.org/P4310319965 |
| locations[0].source.host_organization_name | Springer Nature |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319965 |
| locations[0].source.host_organization_lineage_names | Springer Nature |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| 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 | Visual Computing for Industry, Biomedicine, and Art |
| locations[0].landing_page_url | https://doi.org/10.1186/s42492-024-00169-4 |
| locations[1].id | pmid:39017765 |
| 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 | Visual computing for industry, biomedicine, and art |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/39017765 |
| locations[2].id | pmh:oai:doaj.org/article:70279b117fa5410184260619aab3d1e7 |
| 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 | Visual Computing for Industry, Biomedicine, and Art, Vol 7, Iss 1, Pp 1-13 (2024) |
| locations[2].landing_page_url | https://doaj.org/article/70279b117fa5410184260619aab3d1e7 |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:11255163 |
| 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 | Vis Comput Ind Biomed Art |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11255163 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5059496118 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1770-8905 |
| authorships[0].author.display_name | Muhammad Ramzan |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I139660479 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Computer Science and Engineering, Central South University, Changsha, 410017, Hunan, China |
| authorships[0].institutions[0].id | https://openalex.org/I139660479 |
| authorships[0].institutions[0].ror | https://ror.org/00f1zfq44 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I139660479 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Central South University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Muhammad Ramzan |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Computer Science and Engineering, Central South University, Changsha, 410017, Hunan, China |
| authorships[1].author.id | https://openalex.org/A5043265354 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-6533-7822 |
| authorships[1].author.display_name | Jinfang Sheng |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I139660479 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Computer Science and Engineering, Central South University, Changsha, 410017, Hunan, China |
| authorships[1].institutions[0].id | https://openalex.org/I139660479 |
| authorships[1].institutions[0].ror | https://ror.org/00f1zfq44 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I139660479 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Central South University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Jinfang Sheng |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Computer Science and Engineering, Central South University, Changsha, 410017, Hunan, China |
| authorships[2].author.id | https://openalex.org/A5066349202 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-0308-3035 |
| authorships[2].author.display_name | Muhammad Usman Saeed |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I139660479 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Computer Science and Engineering, Central South University, Changsha, 410017, Hunan, China |
| authorships[2].institutions[0].id | https://openalex.org/I139660479 |
| authorships[2].institutions[0].ror | https://ror.org/00f1zfq44 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I139660479 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Central South University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Muhammad Usman Saeed |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Computer Science and Engineering, Central South University, Changsha, 410017, Hunan, China |
| authorships[3].author.id | https://openalex.org/A5100645749 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-4957-9987 |
| authorships[3].author.display_name | Bin Wang |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I139660479 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Computer Science and Engineering, Central South University, Changsha, 410017, Hunan, China |
| authorships[3].institutions[0].id | https://openalex.org/I139660479 |
| authorships[3].institutions[0].ror | https://ror.org/00f1zfq44 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I139660479 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Central South University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Bin Wang |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Computer Science and Engineering, Central South University, Changsha, 410017, Hunan, China |
| authorships[4].author.id | https://openalex.org/A5104647503 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Faisal Z. Duraihem |
| authorships[4].countries | SA |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I28022161 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Mathematics, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia |
| authorships[4].institutions[0].id | https://openalex.org/I28022161 |
| authorships[4].institutions[0].ror | https://ror.org/02f81g417 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I28022161 |
| authorships[4].institutions[0].country_code | SA |
| authorships[4].institutions[0].display_name | King Saud University |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Faisal Z. Duraihem |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Mathematics, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1186/s42492-024-00169-4 |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Revolutionizing anemia detection: integrative machine learning models and advanced attention mechanisms |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T13702 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9994999766349792 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Machine Learning in Healthcare |
| related_works | https://openalex.org/W4391375266, https://openalex.org/W2748952813, https://openalex.org/W2961085424, https://openalex.org/W4306674287, https://openalex.org/W3046775127, https://openalex.org/W3107602296, https://openalex.org/W4394896187, https://openalex.org/W3170094116, https://openalex.org/W4386462264, https://openalex.org/W4364306694 |
| cited_by_count | 20 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 15 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 5 |
| locations_count | 4 |
| best_oa_location.id | doi:10.1186/s42492-024-00169-4 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S3035464634 |
| best_oa_location.source.issn | 2096-496X, 2524-4442 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2096-496X |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Visual Computing for Industry Biomedicine and Art |
| best_oa_location.source.host_organization | https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_name | Springer Nature |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_lineage_names | Springer Nature |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| 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 | Visual Computing for Industry, Biomedicine, and Art |
| best_oa_location.landing_page_url | https://doi.org/10.1186/s42492-024-00169-4 |
| primary_location.id | doi:10.1186/s42492-024-00169-4 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S3035464634 |
| primary_location.source.issn | 2096-496X, 2524-4442 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2096-496X |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Visual Computing for Industry Biomedicine and Art |
| primary_location.source.host_organization | https://openalex.org/P4310319965 |
| primary_location.source.host_organization_name | Springer Nature |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | Springer Nature |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| 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 | Visual Computing for Industry, Biomedicine, and Art |
| primary_location.landing_page_url | https://doi.org/10.1186/s42492-024-00169-4 |
| publication_date | 2024-07-17 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2039781780, https://openalex.org/W2909769409, https://openalex.org/W3140486334, https://openalex.org/W3169723565, https://openalex.org/W2017703808, https://openalex.org/W4367679088, https://openalex.org/W4378714153, https://openalex.org/W2904782971, https://openalex.org/W4214748244, https://openalex.org/W3038004684, https://openalex.org/W4391294374, https://openalex.org/W4308724293, https://openalex.org/W4390062404, https://openalex.org/W4323309910, https://openalex.org/W3014679960, https://openalex.org/W4311102276, https://openalex.org/W4244993737, https://openalex.org/W4379230732, https://openalex.org/W2964796060, https://openalex.org/W4306767161, https://openalex.org/W4382564455, https://openalex.org/W3019417402, https://openalex.org/W2901330522, https://openalex.org/W4302011877, https://openalex.org/W3038123542, https://openalex.org/W4226287331, https://openalex.org/W2610332124, https://openalex.org/W4296482396, https://openalex.org/W4302307333, https://openalex.org/W4361271148, https://openalex.org/W4386253020, https://openalex.org/W3213012762, https://openalex.org/W3096409984, https://openalex.org/W4317838147, https://openalex.org/W59740843, https://openalex.org/W1976756294 |
| referenced_works_count | 36 |
| abstract_inverted_index.a | 16, 178 |
| abstract_inverted_index.F1 | 102 |
| abstract_inverted_index.In | 110 |
| abstract_inverted_index.ML | 54 |
| abstract_inverted_index.an | 112, 137 |
| abstract_inverted_index.as | 35, 60 |
| abstract_inverted_index.in | 76, 170 |
| abstract_inverted_index.of | 7, 53, 140, 152, 158 |
| abstract_inverted_index.on | 40 |
| abstract_inverted_index.to | 87, 123, 145, 190 |
| abstract_inverted_index.The | 47, 90, 150 |
| abstract_inverted_index.and | 31, 45, 72, 84, 101, 107, 118, 166, 180 |
| abstract_inverted_index.are | 43 |
| abstract_inverted_index.for | 104, 183 |
| abstract_inverted_index.its | 143 |
| abstract_inverted_index.key | 159 |
| abstract_inverted_index.the | 4, 51, 125, 129, 156, 163, 191 |
| abstract_inverted_index.was | 121 |
| abstract_inverted_index.– | 55, 75, 161, 169 |
| abstract_inverted_index.(ML) | 13 |
| abstract_inverted_index.This | 1, 28 |
| abstract_inverted_index.both | 105 |
| abstract_inverted_index.data | 120 |
| abstract_inverted_index.high | 97 |
| abstract_inverted_index.rely | 39 |
| abstract_inverted_index.such | 59 |
| abstract_inverted_index.that | 38, 115 |
| abstract_inverted_index.this | 175 |
| abstract_inverted_index.with | 20, 78 |
| abstract_inverted_index.blood | 18 |
| abstract_inverted_index.found | 122 |
| abstract_inverted_index.image | 108, 119 |
| abstract_inverted_index.issue | 6 |
| abstract_inverted_index.model | 135, 172 |
| abstract_inverted_index.often | 25 |
| abstract_inverted_index.study | 2, 49, 176 |
| abstract_inverted_index.using | 10 |
| abstract_inverted_index.Bayes, | 71 |
| abstract_inverted_index.Naïve | 70 |
| abstract_inverted_index.anemia | 8, 24, 148, 185 |
| abstract_inverted_index.detect | 88 |
| abstract_inverted_index.field. | 192 |
| abstract_inverted_index.health | 22 |
| abstract_inverted_index.manual | 41 |
| abstract_inverted_index.models | 80, 92 |
| abstract_inverted_index.random | 65 |
| abstract_inverted_index.scores | 103 |
| abstract_inverted_index.timely | 30 |
| abstract_inverted_index.trees, | 64 |
| abstract_inverted_index.vector | 68 |
| abstract_inverted_index.99.58%, | 141 |
| abstract_inverted_index.AlexNet | 131 |
| abstract_inverted_index.Spatial | 133 |
| abstract_inverted_index.anemia. | 89 |
| abstract_inverted_index.confirm | 155 |
| abstract_inverted_index.forest, | 66 |
| abstract_inverted_index.machine | 11 |
| abstract_inverted_index.models, | 58 |
| abstract_inverted_index.modules | 83 |
| abstract_inverted_index.present | 48 |
| abstract_inverted_index.recall, | 100 |
| abstract_inverted_index.remains | 26 |
| abstract_inverted_index.results | 151 |
| abstract_inverted_index.spatial | 85, 167 |
| abstract_inverted_index.studies | 154 |
| abstract_inverted_index.support | 67 |
| abstract_inverted_index.textual | 106, 117 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Although | 15 |
| abstract_inverted_index.Multiple | 132 |
| abstract_inverted_index.Overall, | 174 |
| abstract_inverted_index.ablation | 153 |
| abstract_inverted_index.accuracy | 139 |
| abstract_inverted_index.achieved | 136 |
| abstract_inverted_index.approach | 114 |
| abstract_inverted_index.combines | 116 |
| abstract_inverted_index.critical | 5 |
| abstract_inverted_index.decision | 63 |
| abstract_inverted_index.disorder | 19 |
| abstract_inverted_index.explored | 50 |
| abstract_inverted_index.insights | 189 |
| abstract_inverted_index.learning | 12 |
| abstract_inverted_index.logistic | 61 |
| abstract_inverted_index.methods, | 34 |
| abstract_inverted_index.presents | 177 |
| abstract_inverted_index.proposed | 91, 130 |
| abstract_inverted_index.results, | 95 |
| abstract_inverted_index.valuable | 188 |
| abstract_inverted_index.Attention | 134 |
| abstract_inverted_index.accuracy, | 98 |
| abstract_inverted_index.achieving | 96 |
| abstract_inverted_index.addition, | 111 |
| abstract_inverted_index.addresses | 3 |
| abstract_inverted_index.attention | 82, 86 |
| abstract_inverted_index.automated | 147 |
| abstract_inverted_index.datasets. | 109 |
| abstract_inverted_index.detection | 9 |
| abstract_inverted_index.efficient | 32 |
| abstract_inverted_index.enhancing | 171 |
| abstract_inverted_index.framework | 182 |
| abstract_inverted_index.including | 162 |
| abstract_inverted_index.k-nearest | 73 |
| abstract_inverted_index.machines, | 69 |
| abstract_inverted_index.multiple, | 165 |
| abstract_inverted_index.neighbors | 74 |
| abstract_inverted_index.potential | 144 |
| abstract_inverted_index.promising | 94 |
| abstract_inverted_index.approaches | 37 |
| abstract_inverted_index.assessment | 42 |
| abstract_inverted_index.attentions | 168 |
| abstract_inverted_index.components | 160 |
| abstract_inverted_index.detection, | 186 |
| abstract_inverted_index.detection. | 149 |
| abstract_inverted_index.diagnostic | 33 |
| abstract_inverted_index.individual | 126 |
| abstract_inverted_index.innovative | 79, 181 |
| abstract_inverted_index.integrated | 113 |
| abstract_inverted_index.outperform | 124 |
| abstract_inverted_index.precision, | 99 |
| abstract_inverted_index.widespread | 17 |
| abstract_inverted_index.application | 52 |
| abstract_inverted_index.conjunction | 77 |
| abstract_inverted_index.emphasizing | 142 |
| abstract_inverted_index.exceptional | 138 |
| abstract_inverted_index.modalities. | 127 |
| abstract_inverted_index.noninvasive | 184 |
| abstract_inverted_index.regression, | 62 |
| abstract_inverted_index.significant | 21 |
| abstract_inverted_index.subjective. | 46 |
| abstract_inverted_index.techniques. | 14 |
| abstract_inverted_index.traditional | 36 |
| abstract_inverted_index.undetected. | 27 |
| abstract_inverted_index.contributing | 187 |
| abstract_inverted_index.demonstrated | 93 |
| abstract_inverted_index.necessitates | 29 |
| abstract_inverted_index.particularly | 56 |
| abstract_inverted_index.performance. | 173 |
| abstract_inverted_index.significance | 157 |
| abstract_inverted_index.Specifically, | 128 |
| abstract_inverted_index.comprehensive | 179 |
| abstract_inverted_index.implications, | 23 |
| abstract_inverted_index.incorporating | 81 |
| abstract_inverted_index.revolutionize | 146 |
| abstract_inverted_index.classification | 57 |
| abstract_inverted_index.time-consuming | 44 |
| abstract_inverted_index.blue-green-red, | 164 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.98310205 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |