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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