2024
DOI: 10.1186/s13071-024-06215-7
|View full text |Cite
|
Sign up to set email alerts
|

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

Dhevisha Sukumarran,
Khairunnisa Hasikin,
Anis Salwa Mohd Khairuddin
et al.

Abstract: 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 malaria diagnosis toolbox. Malaria persists in many parts of the world, emphasising the urgent need for sophisticated and automated diagnostic instruments to expedite the identification of infected cel… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
4
1

Relationship

0
5

Authors

Journals

citations
Cited by 7 publications
references
References 58 publications
0
0
0
Order By: Relevance