The DOI matches its Crossref record; 99 of the 99 references it cites were found in a registry or an index.
Image analysis and machine learning for detecting malaria
Open accessAbstract
Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis.
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Citations
Cites
Handbook Of Biological Confocal Microscopy
(2006) · Springer eBooks · doi:10.1007/978-0-387-45524-2 · Cited by 3,637
A Review of Malaria Diagnostic Tools: Microscopy and Rapid Diagnostic Test (RDT)
Wongsrichanalai, C., Barcus, M. J., Muth, S., Sutamihardja, A., & Wernsdorfer, W. H. (2007) · The American Journal of Tropical Medicine and Hygiene · doi:10.4269/ajtmh.2007.77.119 · Cited by 939
Mobile Phone Based Clinical Microscopy for Global Health Applications
Breslauer, D. N., Maamari, R. N., Switz, N. A., Lam, W. A., & Fletcher, D. A. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006320 · Cited by 694
Accuracy and precision in quantitative fluorescence microscopy
Waters, J. C. (2009) · Journal of Cell Biology · doi:10.1083/jcb.200903097 · Cited by 613
Malaria Diagnosis: A Brief Review
Tangpukdee, N., Duangdee, C., Wilairatana, P., & Krudsood, S. (2009) · The Korean Journal of Parasitology · doi:10.3347/kjp.2009.47.2.93 · Cited by 581
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Mudanyali, O., Dimitrov, S., Sikora, U., Padmanabhan, S., Navruz, I., & Ozcan, A. (2012) · Lab on a Chip · doi:10.1039/c2lc40235a · Cited by 426
A Quantitative Analysis of the Microvascular Sequestration of Malaria Parasites in the Human Brain
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Self-citationCNN-based image analysis for malaria diagnosis
Liang, Z., Powell, A., Ersoy, I., Poostchi, M., Silamut, K., Palaniappan, K., Guo, P., Hossain, M. A., Sameer, A., Maude, R. J., et al. (2016) · 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2016.7822567 · Cited by 325
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Cited by
Precision medicine, AI, and the future of personalized health care
Johnson, K. B., Wei, W., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021) · Clinical and Translational Science · doi:10.1111/cts.12884 · Cited by 1,854
Rajaraman, S., Antani, S. K., Poostchi, M., Silamut, K., Hossain, M. A., Maude, R. J., Jaeger, S., & Thoma, G. R. (2018) · PeerJ · doi:10.7717/peerj.4568 · Cited by 566
Self-citationPneumonia Detection Using CNN based Feature Extraction
Varshni, D., Thakral, K., Agarwal, L., Nijhawan, R., & Mittal, A. (2019) · 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) · doi:10.1109/icecct.2019.8869364 · Cited by 437
The Effects of Autonomy and Task meaning in Algorithmic Management of Crowdwork
Toyoda, Y., Lucas, G., & Gratch, J. (2020) · International Joint Conference on Autonomous Agents and Multiagent Systems · doi:10.65109/anak9407 · Cited by 11
Metadata
- Authors
- Poostchi, M., Silamut, K., Maude, R. J., Jaeger, S., & Thoma, G.
- Venue
- Translational Research
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 194
- Pages
- 36-55
- ISSN
- 1878-1810 (ISSN-L)
- 1931-5244
- OpenAlex
- W2783699776
- Cited by
- 559
- References
- 99
- FWCI
- 29.16
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Poostchi, M., Silamut, K., Maude, R. J., Jaeger, S., & Thoma, G. (2018). Image analysis and machine learning for detecting malaria. Translational Research. https://doi.org/10.1016/j.trsl.2017.12.004
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
