An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images.
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| Title: | An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images. |
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| Authors: | Sukumarran D; Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia., Hasikin K; Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia. khairunnisa@um.edu.my.; Center of Intelligent Systems for Emerging Technology (CISET), Faculty of Engineering, Universiti Malaya, 50603, Kuala Lumpur, Malaysia. khairunnisa@um.edu.my., Khairuddin ASM; Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.; Malaria Research Centre, Faculty of Medicine and Health Sciences, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia., Ngui R; Department of Para-Clinical Sciences, Faculty of Medicine and Health Sciences, Universiti Malaysia Sarawak, Sarawak, Malaysia. nromano@unimas.my., Sulaiman WYW; Department of Parasitology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia., Vythilingam I; Department of Parasitology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia., Divis PCS; Malaria Research Centre, Faculty of Medicine and Health Sciences, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia. |
| Source: | Parasites & vectors [Parasit Vectors] 2024 Apr 16; Vol. 17 (1), pp. 188. Date of Electronic Publication: 2024 Apr 16. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: BioMed Central Country of Publication: England NLM ID: 101462774 Publication Model: Electronic Cited Medium: Internet ISSN: 1756-3305 (Electronic) Linking ISSN: 17563305 NLM ISO Abbreviation: Parasit Vectors Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 38627870 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Sukumarran+D%22">Sukumarran D</searchLink>; Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.<br /><searchLink fieldCode="AU" term="%22Hasikin+K%22">Hasikin K</searchLink>; Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia. khairunnisa@um.edu.my.; Center of Intelligent Systems for Emerging Technology (CISET), Faculty of Engineering, Universiti Malaya, 50603, Kuala Lumpur, Malaysia. khairunnisa@um.edu.my.<br /><searchLink fieldCode="AU" term="%22Khairuddin+ASM%22">Khairuddin ASM</searchLink>; Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.; Malaria Research Centre, Faculty of Medicine and Health Sciences, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia.<br /><searchLink fieldCode="AU" term="%22Ngui+R%22">Ngui R</searchLink>; Department of Para-Clinical Sciences, Faculty of Medicine and Health Sciences, Universiti Malaysia Sarawak, Sarawak, Malaysia. nromano@unimas.my.<br /><searchLink fieldCode="AU" term="%22Sulaiman+WYW%22">Sulaiman WYW</searchLink>; Department of Parasitology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.<br /><searchLink fieldCode="AU" term="%22Vythilingam+I%22">Vythilingam I</searchLink>; Department of Parasitology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.<br /><searchLink fieldCode="AU" term="%22Divis+PCS%22">Divis PCS</searchLink>; Malaria Research Centre, Faculty of Medicine and Health Sciences, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22101462774%22">Parasites & vectors</searchLink> [Parasit Vectors] 2024 Apr 16; Vol. 17 (1), pp. 188. <i>Date of Electronic Publication: </i>2024 Apr 16. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22BioMed+Central%22">BioMed Central </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>101462774 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1756-3305 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2217563305%22">17563305 </searchLink><i>NLM ISO Abbreviation: </i>Parasit Vectors <i>Subsets: </i>MEDLINE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=38627870 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s13071-024-06215-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 188 Titles: – TitleFull: An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sukumarran D – PersonEntity: Name: NameFull: Hasikin K – PersonEntity: Name: NameFull: Khairuddin ASM – PersonEntity: Name: NameFull: Ngui R – PersonEntity: Name: NameFull: Sulaiman WYW – PersonEntity: Name: NameFull: Vythilingam I – PersonEntity: Name: NameFull: Divis PCS IsPartOfRelationships: – BibEntity: Dates: – D: 16 M: 04 Text: 2024 Apr 16 Type: published Y: 2024 Identifiers: – Type: issn-electronic Value: 1756-3305 Numbering: – Type: volume Value: 17 – Type: issue Value: 1 Titles: – TitleFull: Parasites & vectors Type: main |
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