Selective Intensity Ensemble Classifier (SIEC): A Triple-Threshold Strategy for Microscopic Malaria Cell Image Classification Article Swipe
Abdulaziz Anorboev
,
Sarvinoz Anorboeva
,
Javokhir Musaev
,
Esanbay Usmanov
,
Dosam Hwang
,
Yeong‐Seok Seo
,
Jeongkyu Hong
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3574528
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3574528
Accurate malaria detection in resource-limited settings requires robust solutions—here, we introduce a selective intensity ensemble classifier (SIEC) that applies a triple-threshold strategy for enhanced microscopic image classification. This involves training three separate convolutional neural network models on the same images processed with different pixel-intensity thresholds: original, pixels above 100, and pixels above 200. This approach enables the ensemble to capture complementary low-, mid-, and high-intensity features, enhancing feature diversity and classification accuracy. The experiments were conducted on the publicly available Malaria Cell Dataset, consisting of 27,558 images. The proposed SIEC achieved an accuracy of 95.09%, with a precision of 95.27%, and matching recall and F1 scores of 95.09%, consistently outperforming six standard CNN models, including ResNet50, VGG16, Inception, and MobileNetV2. Notably, the combination of the 100-pixel filtered and original images yielded the highest classification performance, demonstrating the ensemble’s ability to integrate detailed and abstracted features effectively. These findings highlight SIEC as a promising and scalable solution for automated malaria detection and broader diagnostic imaging tasks.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2025.3574528
- OA Status
- gold
- Cited By
- 1
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410808710
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4410808710Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2025.3574528Digital Object Identifier
- Title
-
Selective Intensity Ensemble Classifier (SIEC): A Triple-Threshold Strategy for Microscopic Malaria Cell Image ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-01Full publication date if available
- Authors
-
Abdulaziz Anorboev, Sarvinoz Anorboeva, Javokhir Musaev, Esanbay Usmanov, Dosam Hwang, Yeong‐Seok Seo, Jeongkyu HongList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2025.3574528Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1109/access.2025.3574528Direct OA link when available
- Concepts
-
Artificial intelligence, Pattern recognition (psychology), Computer science, Contextual image classification, Classifier (UML), Computer vision, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4410808710 |
|---|---|
| doi | https://doi.org/10.1109/access.2025.3574528 |
| ids.doi | https://doi.org/10.1109/access.2025.3574528 |
| ids.openalex | https://openalex.org/W4410808710 |
| fwci | 4.81974515 |
| type | article |
| title | Selective Intensity Ensemble Classifier (SIEC): A Triple-Threshold Strategy for Microscopic Malaria Cell Image Classification |
| awards[0].id | https://openalex.org/G700460555 |
| awards[0].funder_id | https://openalex.org/F4320322120 |
| awards[0].display_name | |
| awards[0].funder_award_id | NRF-2023R1A2C1008134 |
| awards[0].funder_display_name | National Research Foundation of Korea |
| awards[1].id | https://openalex.org/G1444696939 |
| awards[1].funder_id | https://openalex.org/F4320322120 |
| awards[1].display_name | |
| awards[1].funder_award_id | RS-2023-NR076933 |
| awards[1].funder_display_name | National Research Foundation of Korea |
| biblio.issue | |
| biblio.volume | 13 |
| biblio.last_page | 101623 |
| biblio.first_page | 101609 |
| topics[0].id | https://openalex.org/T12761 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9883999824523926 |
| 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 | Data Stream Mining Techniques |
| 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 | 0.9585999846458435 |
| 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/T10640 |
| topics[2].field.id | https://openalex.org/fields/16 |
| topics[2].field.display_name | Chemistry |
| topics[2].score | 0.9476000070571899 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1602 |
| topics[2].subfield.display_name | Analytical Chemistry |
| topics[2].display_name | Spectroscopy and Chemometric Analyses |
| funders[0].id | https://openalex.org/F4320322120 |
| funders[0].ror | https://ror.org/013aysd81 |
| funders[0].display_name | National Research Foundation of Korea |
| is_xpac | False |
| apc_list.value | 1850 |
| apc_list.currency | USD |
| apc_list.value_usd | 1850 |
| apc_paid.value | 1850 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 1850 |
| concepts[0].id | https://openalex.org/C154945302 |
| concepts[0].level | 1 |
| concepts[0].score | 0.6720824241638184 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[0].display_name | Artificial intelligence |
| concepts[1].id | https://openalex.org/C153180895 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6148518919944763 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[1].display_name | Pattern recognition (psychology) |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6002867221832275 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C75294576 |
| concepts[3].level | 3 |
| concepts[3].score | 0.4800735116004944 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q5165192 |
| concepts[3].display_name | Contextual image classification |
| concepts[4].id | https://openalex.org/C95623464 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4659467935562134 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1096149 |
| concepts[4].display_name | Classifier (UML) |
| concepts[5].id | https://openalex.org/C31972630 |
| concepts[5].level | 1 |
| concepts[5].score | 0.3294528126716614 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[5].display_name | Computer vision |
| concepts[6].id | https://openalex.org/C115961682 |
| concepts[6].level | 2 |
| concepts[6].score | 0.22379523515701294 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[6].display_name | Image (mathematics) |
| keywords[0].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[0].score | 0.6720824241638184 |
| keywords[0].display_name | Artificial intelligence |
| keywords[1].id | https://openalex.org/keywords/pattern-recognition |
| keywords[1].score | 0.6148518919944763 |
| keywords[1].display_name | Pattern recognition (psychology) |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6002867221832275 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/contextual-image-classification |
| keywords[3].score | 0.4800735116004944 |
| keywords[3].display_name | Contextual image classification |
| keywords[4].id | https://openalex.org/keywords/classifier |
| keywords[4].score | 0.4659467935562134 |
| keywords[4].display_name | Classifier (UML) |
| keywords[5].id | https://openalex.org/keywords/computer-vision |
| keywords[5].score | 0.3294528126716614 |
| keywords[5].display_name | Computer vision |
| keywords[6].id | https://openalex.org/keywords/image |
| keywords[6].score | 0.22379523515701294 |
| keywords[6].display_name | Image (mathematics) |
| language | en |
| locations[0].id | doi:10.1109/access.2025.3574528 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2485537415 |
| locations[0].source.issn | 2169-3536 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2169-3536 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | IEEE Access |
| locations[0].source.host_organization | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_name | Institute of Electrical and Electronics Engineers |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| 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 | IEEE Access |
| locations[0].landing_page_url | https://doi.org/10.1109/access.2025.3574528 |
| locations[1].id | pmh:oai:doaj.org/article:1fdfc75fd162484fa7e177d440dfe3c0 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| 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 | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | IEEE Access, Vol 13, Pp 101609-101623 (2025) |
| locations[1].landing_page_url | https://doaj.org/article/1fdfc75fd162484fa7e177d440dfe3c0 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5084255344 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1416-7138 |
| authorships[0].author.display_name | Abdulaziz Anorboev |
| authorships[0].countries | KR |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I55240360 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea |
| authorships[0].institutions[0].id | https://openalex.org/I55240360 |
| authorships[0].institutions[0].ror | https://ror.org/05yc6p159 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I55240360 |
| authorships[0].institutions[0].country_code | KR |
| authorships[0].institutions[0].display_name | Yeungnam University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Abdulaziz Anorboev |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea |
| authorships[1].author.id | https://openalex.org/A5092640563 |
| authorships[1].author.orcid | https://orcid.org/0009-0007-5038-7038 |
| authorships[1].author.display_name | Sarvinoz Anorboeva |
| authorships[1].countries | KR |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I55240360 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea |
| authorships[1].institutions[0].id | https://openalex.org/I55240360 |
| authorships[1].institutions[0].ror | https://ror.org/05yc6p159 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I55240360 |
| authorships[1].institutions[0].country_code | KR |
| authorships[1].institutions[0].display_name | Yeungnam University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Sarvinoz Anorboeva |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea |
| authorships[2].author.id | https://openalex.org/A5091106383 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-4656-0479 |
| authorships[2].author.display_name | Javokhir Musaev |
| authorships[2].countries | KR |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I55240360 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea |
| authorships[2].institutions[0].id | https://openalex.org/I55240360 |
| authorships[2].institutions[0].ror | https://ror.org/05yc6p159 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I55240360 |
| authorships[2].institutions[0].country_code | KR |
| authorships[2].institutions[0].display_name | Yeungnam University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Javokhir Musaev |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea |
| authorships[3].author.id | https://openalex.org/A5117313094 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Esanbay Usmanov |
| authorships[3].affiliations[0].raw_affiliation_string | School of Pedagogy and Languages, Angren University, Angren, Uzbekistan |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Esanbay Usmanov |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Pedagogy and Languages, Angren University, Angren, Uzbekistan |
| authorships[4].author.id | https://openalex.org/A5032265881 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-7851-7323 |
| authorships[4].author.display_name | Dosam Hwang |
| authorships[4].countries | KR |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I55240360 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea |
| authorships[4].institutions[0].id | https://openalex.org/I55240360 |
| authorships[4].institutions[0].ror | https://ror.org/05yc6p159 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I55240360 |
| authorships[4].institutions[0].country_code | KR |
| authorships[4].institutions[0].display_name | Yeungnam University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Dosam Hwang |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea |
| authorships[5].author.id | https://openalex.org/A5012219094 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-5319-7674 |
| authorships[5].author.display_name | Yeong‐Seok Seo |
| authorships[5].countries | KR |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I55240360 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea |
| authorships[5].institutions[0].id | https://openalex.org/I55240360 |
| authorships[5].institutions[0].ror | https://ror.org/05yc6p159 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I55240360 |
| authorships[5].institutions[0].country_code | KR |
| authorships[5].institutions[0].display_name | Yeungnam University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Yeong-Seok Seo |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea |
| authorships[6].author.id | https://openalex.org/A5019410243 |
| authorships[6].author.orcid | https://orcid.org/0000-0003-4541-2255 |
| authorships[6].author.display_name | Jeongkyu Hong |
| authorships[6].countries | KR |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I124633538 |
| authorships[6].affiliations[0].raw_affiliation_string | School of Electrical and Computer Engineering, University of Seoul, Seoul, South Korea |
| authorships[6].institutions[0].id | https://openalex.org/I124633538 |
| authorships[6].institutions[0].ror | https://ror.org/05en5nh73 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I124633538 |
| authorships[6].institutions[0].country_code | KR |
| authorships[6].institutions[0].display_name | University of Seoul |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Jeongkyu Hong |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | School of Electrical and Computer Engineering, University of Seoul, Seoul, South Korea |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1109/access.2025.3574528 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Selective Intensity Ensemble Classifier (SIEC): A Triple-Threshold Strategy for Microscopic Malaria Cell Image Classification |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12761 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9883999824523926 |
| 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 | Data Stream Mining Techniques |
| related_works | https://openalex.org/W2772917594, https://openalex.org/W2036807459, https://openalex.org/W2058170566, https://openalex.org/W2755342338, https://openalex.org/W2166024367, https://openalex.org/W3116076068, https://openalex.org/W2229312674, https://openalex.org/W2951359407, https://openalex.org/W2079911747, https://openalex.org/W1969923398 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1109/access.2025.3574528 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2485537415 |
| best_oa_location.source.issn | 2169-3536 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2169-3536 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | IEEE Access |
| best_oa_location.source.host_organization | https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| 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 | IEEE Access |
| best_oa_location.landing_page_url | https://doi.org/10.1109/access.2025.3574528 |
| primary_location.id | doi:10.1109/access.2025.3574528 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2485537415 |
| primary_location.source.issn | 2169-3536 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2169-3536 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | IEEE Access |
| primary_location.source.host_organization | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| 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 | IEEE Access |
| primary_location.landing_page_url | https://doi.org/10.1109/access.2025.3574528 |
| publication_date | 2025-01-01 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4242720327, https://openalex.org/W3038010422, https://openalex.org/W4384572151, https://openalex.org/W4402301228, https://openalex.org/W4395096776, https://openalex.org/W4296558701, https://openalex.org/W4282938680, https://openalex.org/W3214312079, https://openalex.org/W2585528949, https://openalex.org/W2980444207, https://openalex.org/W4308237781, https://openalex.org/W3177177354, https://openalex.org/W3049501660, https://openalex.org/W3027312415, https://openalex.org/W3088070507, https://openalex.org/W4312612636, https://openalex.org/W2945989246, https://openalex.org/W3181029726, https://openalex.org/W4282966661, https://openalex.org/W4366986782, https://openalex.org/W3005404186, https://openalex.org/W4220879982, https://openalex.org/W3215876370, https://openalex.org/W3135627022, https://openalex.org/W4296499376, https://openalex.org/W4385832651, https://openalex.org/W4319431271 |
| referenced_works_count | 27 |
| abstract_inverted_index.a | 11, 19, 96, 151 |
| abstract_inverted_index.F1 | 104 |
| abstract_inverted_index.an | 91 |
| abstract_inverted_index.as | 150 |
| abstract_inverted_index.in | 3 |
| abstract_inverted_index.of | 84, 93, 98, 106, 123 |
| abstract_inverted_index.on | 36, 76 |
| abstract_inverted_index.to | 58, 139 |
| abstract_inverted_index.we | 9 |
| abstract_inverted_index.CNN | 112 |
| abstract_inverted_index.The | 72, 87 |
| abstract_inverted_index.and | 49, 63, 69, 100, 103, 118, 127, 142, 153, 160 |
| abstract_inverted_index.for | 22, 156 |
| abstract_inverted_index.six | 110 |
| abstract_inverted_index.the | 37, 56, 77, 121, 124, 131, 136 |
| abstract_inverted_index.100, | 48 |
| abstract_inverted_index.200. | 52 |
| abstract_inverted_index.Cell | 81 |
| abstract_inverted_index.SIEC | 89, 149 |
| abstract_inverted_index.This | 27, 53 |
| abstract_inverted_index.same | 38 |
| abstract_inverted_index.that | 17 |
| abstract_inverted_index.were | 74 |
| abstract_inverted_index.with | 41, 95 |
| abstract_inverted_index.These | 146 |
| abstract_inverted_index.above | 47, 51 |
| abstract_inverted_index.image | 25 |
| abstract_inverted_index.low-, | 61 |
| abstract_inverted_index.mid-, | 62 |
| abstract_inverted_index.three | 30 |
| abstract_inverted_index.(SIEC) | 16 |
| abstract_inverted_index.27,558 | 85 |
| abstract_inverted_index.VGG16, | 116 |
| abstract_inverted_index.images | 39, 129 |
| abstract_inverted_index.models | 35 |
| abstract_inverted_index.neural | 33 |
| abstract_inverted_index.pixels | 46, 50 |
| abstract_inverted_index.recall | 102 |
| abstract_inverted_index.robust | 7 |
| abstract_inverted_index.scores | 105 |
| abstract_inverted_index.tasks. | 164 |
| abstract_inverted_index.95.09%, | 94, 107 |
| abstract_inverted_index.95.27%, | 99 |
| abstract_inverted_index.Malaria | 80 |
| abstract_inverted_index.ability | 138 |
| abstract_inverted_index.applies | 18 |
| abstract_inverted_index.broader | 161 |
| abstract_inverted_index.capture | 59 |
| abstract_inverted_index.enables | 55 |
| abstract_inverted_index.feature | 67 |
| abstract_inverted_index.highest | 132 |
| abstract_inverted_index.images. | 86 |
| abstract_inverted_index.imaging | 163 |
| abstract_inverted_index.malaria | 1, 158 |
| abstract_inverted_index.models, | 113 |
| abstract_inverted_index.network | 34 |
| abstract_inverted_index.yielded | 130 |
| abstract_inverted_index.Accurate | 0 |
| abstract_inverted_index.Dataset, | 82 |
| abstract_inverted_index.Notably, | 120 |
| abstract_inverted_index.accuracy | 92 |
| abstract_inverted_index.achieved | 90 |
| abstract_inverted_index.approach | 54 |
| abstract_inverted_index.detailed | 141 |
| abstract_inverted_index.enhanced | 23 |
| abstract_inverted_index.ensemble | 14, 57 |
| abstract_inverted_index.features | 144 |
| abstract_inverted_index.filtered | 126 |
| abstract_inverted_index.findings | 147 |
| abstract_inverted_index.involves | 28 |
| abstract_inverted_index.matching | 101 |
| abstract_inverted_index.original | 128 |
| abstract_inverted_index.proposed | 88 |
| abstract_inverted_index.publicly | 78 |
| abstract_inverted_index.requires | 6 |
| abstract_inverted_index.scalable | 154 |
| abstract_inverted_index.separate | 31 |
| abstract_inverted_index.settings | 5 |
| abstract_inverted_index.solution | 155 |
| abstract_inverted_index.standard | 111 |
| abstract_inverted_index.strategy | 21 |
| abstract_inverted_index.training | 29 |
| abstract_inverted_index.100-pixel | 125 |
| abstract_inverted_index.ResNet50, | 115 |
| abstract_inverted_index.accuracy. | 71 |
| abstract_inverted_index.automated | 157 |
| abstract_inverted_index.available | 79 |
| abstract_inverted_index.conducted | 75 |
| abstract_inverted_index.detection | 2, 159 |
| abstract_inverted_index.different | 42 |
| abstract_inverted_index.diversity | 68 |
| abstract_inverted_index.enhancing | 66 |
| abstract_inverted_index.features, | 65 |
| abstract_inverted_index.highlight | 148 |
| abstract_inverted_index.including | 114 |
| abstract_inverted_index.integrate | 140 |
| abstract_inverted_index.intensity | 13 |
| abstract_inverted_index.introduce | 10 |
| abstract_inverted_index.original, | 45 |
| abstract_inverted_index.precision | 97 |
| abstract_inverted_index.processed | 40 |
| abstract_inverted_index.promising | 152 |
| abstract_inverted_index.selective | 12 |
| abstract_inverted_index.Inception, | 117 |
| abstract_inverted_index.abstracted | 143 |
| abstract_inverted_index.classifier | 15 |
| abstract_inverted_index.consisting | 83 |
| abstract_inverted_index.diagnostic | 162 |
| abstract_inverted_index.combination | 122 |
| abstract_inverted_index.experiments | 73 |
| abstract_inverted_index.microscopic | 24 |
| abstract_inverted_index.thresholds: | 44 |
| abstract_inverted_index.MobileNetV2. | 119 |
| abstract_inverted_index.consistently | 108 |
| abstract_inverted_index.effectively. | 145 |
| abstract_inverted_index.performance, | 134 |
| abstract_inverted_index.complementary | 60 |
| abstract_inverted_index.convolutional | 32 |
| abstract_inverted_index.demonstrating | 135 |
| abstract_inverted_index.outperforming | 109 |
| abstract_inverted_index.classification | 70, 133 |
| abstract_inverted_index.high-intensity | 64 |
| abstract_inverted_index.classification. | 26 |
| abstract_inverted_index.pixel-intensity | 43 |
| abstract_inverted_index.resource-limited | 4 |
| abstract_inverted_index.triple-threshold | 20 |
| abstract_inverted_index.ensemble’s | 137 |
| abstract_inverted_index.solutions—here, | 8 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.94446399 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |