Microscopic malaria parasitemia diagnosis and grading on benchmark datasets
Abstract
Malaria parasitemia diagnosis and grading is hard and still far from perfection. Inaccurate diagnosis and grading has caused tremendous deaths rate particularly in young children worldwide. The current research deeply reviews automated malaria parasitemia diagnosis and grading in thin blood smear digital images through image analysis and computer vision based techniques. Actually, state-of-the-art reveals that current proposed practices present partially or morphology dependent solutions to the problem of computer vision based microscopy diagnosis of malaria parasitemia. Accordingly, a deep appraisal of the current practices is investigated, compared and analyzed on benchmark datasets. The open gaps are highlighted and the future directions are laid down for a complete automated microscopy diagnosis for malaria parasitemia based on those factors that have not been affected by other diseases. Moreover, a general computer vision framework to perform malaria parasitemia estimation/grading is constructed in universal directions. Finally, remaining problems are highlighted and possible directions are suggested. RESEARCH HIGHLIGHTS: The current research presents a microscopic malaria parasitemia diagnosis and grading of malaria in thin blood smear digital images through image analysis and computer vision based techniques. The open gaps are highlighted and future directions for a complete automated microscopy diagnosis of malaria parasitemia mentioned.
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Determination of parasitemia and classification are difficult and far from perfect; inaccurate diagnosis and classification have caused a high mortality rate, especially in children.
“determination of parasitemia and classification are difficult and far from perfect; inaccurate diagnosis and classification have caused a high mortality rate, especially in children [51,55].”
From Diagnostic Accuracy of a Thick Blood Smear Compared to qPCR for Malaria Associated with Pregnancy in Colombia (2023) · cites this paper as [55]
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Segmentation of haematopoeitic cells in bone marrow using circle detection and splitting techniques
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Stiffness Dependent Separation of Cells in a Microfluidic Device
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Splitting touching-cell clusters on histopathological images
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Flagged first, then by citations
Cited by
Sorted by citations
Cardona-Arias, J. A., Higuita Gutiérrez, L. F., & Carmona-Fonseca, J. (2023) · Tropical Medicine and Infectious Disease · doi:10.3390/tropicalmed8020119 · Cited by 6 · see citing works
- Authors
- Rehman, A., Abbas, N., Saba, T., Mehmood, Z., Mahmood, T., & Ahmed, K. T.
- Affiliations
- Al Yamamah University (SA)
- Islamia College University (PK)
- Prince Sultan University (SA)
- University of Engineering and Technology Taxila (PK)
- Bahauddin Zakariya University (PK)
- Corresponding author
- Rehman, Amjad
- Venue
- Microscopy Research and Technique
- Publisher
- Wiley
- Published
- 2018-09
- Volume
- 81
- Issue
- 9
- Pages
- 1042-1058
- ISSN
- 1059-910X (ISSN-L)
- 1097-0029
- OpenAlex
- W2892057769
- Cited by
- 57
- References
- 132
- Citations by year
- 2026: 2 · 2025: 3 · 2023: 10 · 2022: 13 · 2021: 9 · 2020: 8 · 2019: 11 · 2018: 1
- FWCI
- 0.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Microscopy Research and Technique (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Prince Sultan University (RG-CCIS-2017-06-02)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Engineering › Media Technology › Image Processing Techniques and Applications
- Keywords
- malaria parasitaemia, thin blood smear images, computer vision, image analysis, benchmark dataset, malaria diagnosis
- MeSH terms
- Humans, Image Processing, Computer-Assisted, Malaria, Microscopy, Severity of Illness Index, Parasitemia, Benchmarking
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