Evidence map›Paper›PMID 39896354›Full record

ArticlePeerJ. Computer science2024

Enhancing breast cancer diagnosis using deep learning and gradient multi-verse optimizer: a robust biomedical data analysis approach.

Yassine El Kati, Shu-Lin Wang, Mundher Mohammed Taresh, Talal Ahmed Ali Ali

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Yassine El KatiCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China.
Shu-Lin WangCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China.
Mundher Mohammed TareshCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China.ORCID 0000-0001-8722-4661
Talal Ahmed Ali AliCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer (BC) is one of the most common causes of mortality among women. However, early detection of BC can effectively improve the treatment outcomes. Computer-aided diagnosis (CAD) systems can be utilized clinical specialists for accurate diagnosis of BC in its early stages. Due to their superior classification performance, deep learning (DL) methods have been extensively used in CAD systems. The classification accuracy of a DL model mainly depends on the parameters, such as weights and biases, of the deep neural network (DNN), which are optimized during the training phase. The training of DL models has been carried out by gradient-based techniques,

Indexed as

Deep neural networkGradient searchMetaheuristic algorithmMulti-verse optimizerThe Wisconsin diagnosis breast cancer

Identifiers

PMID39896354
PMCPMC11784758

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.