ArticleJournal of medical imaging (Bellingham, Wash.)2018
Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy.
Article in Journal of medical imaging (Bellingham, Wash.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 48 citations in OpenAlex.
- Application of Machine Learning in a Rodent Malaria Model for Rapid, Accurate, and Consistent Parasite Counts.The American journal of tropical medicine and hygiene · 2024Article
- Competency of malaria laboratory diagnosis at national and provincial levels at the beginning of malaria post-elimination phase, China.Malaria journal · 2024Article
- Evaluating Plasmodium falciparum automatic detection and parasitemia estimation: A comparative study on thin blood smear images.PloS one · 2024Article
- Increasing a microscope's effective field of view via overlapped imaging and machine learning.Optics express · 2022Article
- Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears.IEEE journal of biomedical and health informatics · 2021Article
- Deep Learning-Based Cell Detection and Extraction in Thin Blood Smears for Malaria Diagnosis.IEEE Applied Imagery Pattern Recognition Workshop : [proceedings]. IEEE Applied Imagery Pattern Recognition Workshop · 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
- Malaria Screener: a smartphone application for automated malaria screening.BMC infectious diseases · 2020Article
- Image analysis and artificial intelligence in infectious disease diagnostics.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2020Review
Corrections and comments
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Authors and funding
13 authors at 7 institutions in 5 countries.
Funding
Abstract
Despite the remarkable progress that has been made to reduce global malaria mortality by 29% in the past 5 years, malaria is still a serious global health problem. Inadequate diagnostics is one of the major obstacles in fighting the disease. An automated system for malaria diagnosis can help to make malaria screening faster and more reliable. We present an automated system to detect and segment red blood cells (RBCs) and identify infected cells in Wright-Giemsa stained thin blood smears. Specifically, using image analysis and machine learning techniques, we process digital images of thin blood smears to determine the parasitemia in each smear. We use a cell extraction method to segment RBCs, in particular overlapping cells. We show that a combination of RGB color and texture features outperforms other features. We evaluate our method on microscopic blood smear images from human and mouse and show that it outperforms other techniques. For human cells, we measure an absolute error of 1.18% between the true and the automatic parasite counts. For mouse cells, our automatic counts correlate well with expert and flow cytometry counts. This makes our system the first one to work for both human and mouse.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.