ArticleJournal of digital imaging2020
Real-time Malaria Parasite Screening in Thick Blood Smears for Low-Resource Setting.
Article in Journal of digital imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 45 citations in OpenAlex.
- Deep learning applications in parasite microscopy: A scoping review.PLoS neglected tropical diseases · 2026Article
- Rapid and accurate recognition of erythrocytic stage parasites ofFrontiers in microbiology · 2025Article
- Advances in Malaria Diagnostic Methods in Resource-Limited Settings: A Systematic Review.Tropical medicine and infectious disease · 2024Review
- DeepLeish: a deep learning based support system for the detection of Leishmaniasis parasite from Giemsa-stained microscope images.BMC medical imaging · 2024Article
- Supporting Malaria Diagnosis Using Deep Learning and Data Augmentation.Diagnostics (Basel, Switzerland) · 2024Article
- Automatic patient-level recognition of fourMicrobiology spectrum · 2024Article
- Tile-based microscopic image processing for malaria screening using a deep learning approach.BMC medical imaging · 2023Article
- Patient-level performance evaluation of a smartphone-based malaria diagnostic application.Malaria journal · 2023Article
- Diagnosing Malaria Patients withDiagnostics (Basel, Switzerland) · 2021Article
- Label-free imaging and classification of live P. falciparum enables high performance parasitemia quantification without fixation or staining.PLoS computational biology · 2021Article
- Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models.BMC bioinformatics · 2021Article
- Performance of a fully-automated system on a WHO malaria microscopy evaluation slide set.Malaria journal · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Malaria is a serious public health problem in many parts of the world. Early diagnosis and prompt effective treatment are required to avoid anemia, organ failure, and malaria-associated deaths. Microscopic analysis of blood samples is the preferred method for diagnosis. However, manual microscopic examination is very laborious and requires skilled health personnel of which there is a critical shortage in the developing world such as in sub-Saharan Africa. Critical shortages of trained health personnel and the inability to cope with the workload to examine malaria slides are among the main limitations of malaria microscopy especially in low-resource and high disease burden areas. We present a low-cost alternative and complementary solution for rapid malaria screening for low resource settings to potentially reduce the dependence on manual microscopic examination. We develop an image processing pipeline using a modified YOLOv3 detection algorithm to run in real time on low-cost devices. We test the performance of our solution on two datasets. In the dataset collected using a microscope camera, our model achieved 99.07% accuracy and 97.46% accuracy on the dataset collected using a mobile phone camera. While the mean average precision of our model is on par with human experts at an object level, we are several orders of magnitude faster than human experts as we can detect parasites in images as well as videos in real time.
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