Evidence map›Paper›PMID 31687444›Full record

ArticleData in brief2019

A dataset for automatic contrast enhancement of microscopic malaria infected blood RGB images.

J Somasekar, G Ramesh, Gandikota Ramu, P Dileep Kumar Reddy, B Eswara Reddy, Ching-Hao Lai

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Data in brief, 2019. 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. AneRBC dataset: a benchmark dataset for computer-aided anemia diagnosis using RBC images.Database : the journal of biological databases and curation · 2024
    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

6 authors.

J SomasekarDepartment of Computer Science and Engineering, Gopalan College of Engineering and Management, Whitefield, Bangalore, Karnataka, 560 048, India.
G RameshDepartment of CSE, GRIET, Bachupally, Hyderabad, Telangana, 500 090, India.
Gandikota RamuDepartment of CSE, Institute of Aeronautical Engineering, Hyderabad, Telangana, 500043, India.
P Dileep Kumar ReddyDepartment of CSE, Sri Venkateswara College of Engineering, Karakambadi Road, Tirupati, 517 507, India.
B Eswara ReddyDepartment of CSE, JNTUA College of Engineering Anantapurramu, Anantapuramu, Andhra Pradesh, 515 002, India.
Ching-Hao LaiComputational Intelligence Technology Center, Industrial Technology Research Institute, Chung Hsing Rd., Chutung, Hsinchu, 310 40, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this article we introduce a malaria infected microscopic images dataset for contrast enhancement which assist for malaria diagnosis more accurately. The dataset contains around two hundred malaria infected, normal, species and various stages of microscopic blood images. We propose and experimentally demonstrate a contrast enhancement technique for this dataset. This simple technique increases the contrast of an image and hence, reveals significant information about malaria infected cells. Experiments on the dataset show the superior performance of our proposed method for contrast enhancement of malaria microscopic imaging.

Indexed as

Contrast enhancementHistogram equalizationLow contrast imagesMalaria diagnosisMicroscopic RGB blood images

Identifiers

PMID31687444
PMCPMC6820113

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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.