Real-time Malaria Parasite Screening in Thick Blood Smears for Low-Resource Setting.
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 e...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 3; pp. 763 - 776 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143476523&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143476523 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2020 vid: 33 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143476523 143476523 143476523 10.1007/s10278-019-00284-2 143476523 ppf: 763 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Real-time Malaria Parasite Screening in Thick Blood Smears for Low-Resource Setting. aug: au: Chibuta, Samson Acar, Aybar C. affil: Health Informatics Department, Middle East Technical University, Ankara, Turkey sug: subj: Malaria Blood Parasites Analysis Image Processing, Computer Assisted Methods Microscopy Methods Human Costs and Cost Analysis Sensitivity and Specificity Deep Learning Algorithms ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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