Evidence map›Paper›PMID 39687374›Full record

ArticleData in brief2024

Blood smear imagery dataset for malaria parasite detection: A case of Tanzania.

Beston Lufyagila, Bonny Mgawe, Anael Sam

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Beston LufyagilaDepartment of Information and Communication Sciences and Engineering, The Nelson Mandela African Institution of Science and Technology, PO Box 447, Tengeru, Arusha, Tanzania.
Bonny MgaweDepartment of Information and Communication Sciences and Engineering, The Nelson Mandela African Institution of Science and Technology, PO Box 447, Tengeru, Arusha, Tanzania.
Anael SamDepartment of Information and Communication Sciences and Engineering, The Nelson Mandela African Institution of Science and Technology, PO Box 447, Tengeru, Arusha, Tanzania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Malaria is a major public health issue in many regions of Africa, including Tanzania. The Tanzania Malaria National Strategic Plan (2021-2025) emphasizes on high-quality testing services availability, high coverage of timely diagnosis of malaria, and availability of innovative diagnostic systems for effective detection, treatment and control of malaria. This would be achieved by employing state of the art technologies like Machine learning. However, Machine learning requires diverse dataset to work effectively and efficiently. Therefore, this paper presents blood smear imagery dataset that can be used by researchers to develop computer vision systems for malaria parasite detection. The imagery dataset were acquired by setting up a 40X-2500X Real 4 K compound microscope with a 4k SONY IMX334 sensor camera mounted to it in five health centres of Tanga region. Blood samples taken according to normal routine of diagnosing patients in health care, were stained using Giemsa reagent and examined under microscope. Following these procedures, the study collected and annotated Thick infected blood smear images (

Indexed as

Image annotationMalariaMalaria parasiteMedical diagnosisPlasmodium

Identifiers

PMID39687374
PMCPMC11648091

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.