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An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Background Malaria is a serious public health concern worldwide. Early and accurate diagnosis is essential for controlling the disease’s spread and avoiding severe health complications. Manual examination of blood smear samples by skilled technicians is a time-consuming aspect of the conventional m...

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Bibliographic Details
Main Authors: Dhevisha, Sukumarran, Khairunnisa, Hasikin, Anis Salwa, Mohd Khairuddin, Romano, Ngui, Wan Yusoff, Wan Sulaiman, Indra, Vythilingam, Paul Cliff, Simon Divis
Format: Article
Language:English
Published: BioMed Central Ltd. 2024
Subjects:
RZ Other systems of medicine
Online Access:http://ir.unimas.my/id/eprint/44586/1/2024_Sukumarran%20et%20al_AI%20Malaria%20Detection.pdf
http://ir.unimas.my/id/eprint/44586/
https://parasitesandvectors.biomedcentral.com/articles/10.1186/s13071-024-06215-7
https://doi.org/10.1186/s13071-024-06215-7
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http://ir.unimas.my/id/eprint/44586/1/2024_Sukumarran%20et%20al_AI%20Malaria%20Detection.pdf
http://ir.unimas.my/id/eprint/44586/
https://parasitesandvectors.biomedcentral.com/articles/10.1186/s13071-024-06215-7
https://doi.org/10.1186/s13071-024-06215-7

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