Evidence map›Paper›PMID 39262920›Full record

ArticleComputational intelligence and neuroscience2022

A Lightweight Hybrid Dilated Ghost Model-Based Approach for the Prognosis of Breast Cancer.

Edwin Ramirez-Asis, Romel Percy Melgarejo Bolivar, Leonid Alemán Gonzales, Sushovan Chaudhury, Ramgopal Kashyap, Walaa F Alsanie, G K Viju

RetractedAbstract readRetracted Publication
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Edwin Ramirez-AsisUniversidad Nacional Santiago Antunez de Mayolo, Huaraz, Peru.ORCID https://orcid.org/0000-0002-9918-7607
Romel Percy Melgarejo BolivarFaculty of Statistical Engineering and Computer Science, Computer Science Research Institute, National University of the Altiplano of Puno, P.O. Box 291, Puno, Peru.ORCID https://orcid.org/0000-0001-9383-9136
Leonid Alemán GonzalesFaculty of Statistical Engineering and Computer Science, Computer Science Research Institute, National University of the Altiplano of Puno, P.O. Box 291, Puno, Peru.ORCID https://orcid.org/0000-0002-4072-6370
Sushovan ChaudhuryUniversity of Engineering and Management, Kolkata, India.ORCID https://orcid.org/0000-0002-9336-2654
Ramgopal KashyapAmity University Chhattisgarh, Chhattisgarh, India.
Walaa F AlsanieDepartment of Clinical Laboratory Sciences, The Faculty of Applied Medical Sciences, Taif University, Taif, Saudi Arabia.
G K VijuPost Graduate Studies, University of Garden City, Khartoum, Sudan.ORCID https://orcid.org/0000-0003-3153-0277

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most approaches use interactive priors to find tumours and then segment them based on tumour-centric candidates. A fully convolutional network is demonstrated for end-to-end breast tumour segmentation. When confronted with such a variety of options, to enhance tumour detection in digital mammograms, one uses multiscale picture information.

Identifiers

PMID39262920
PMCPMC11390200

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.