Evidence map›Paper›PMID 34539771›Full record

ArticleComputational intelligence and neuroscience2021

Application of CNN Algorithm Based on Chaotic Recursive Diagonal Model in Medical Image Processing.

Fangfang Ye, Sen Xu, Ting Wang, Zhangquan Wang, Tiaojuan Ren

RetractedAbstract readRetracted Publication
In one paragraph

Article in Computational intelligence and neuroscience, 2021. 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 It has been retracted, and should not be counted.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 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

5 · Who and what money

Authors and funding

5 authors.

Fangfang YeSchool of Information and Science Technology, Zhejiang Shuren University, Hangzhou 310015, Zhejiang, China.ORCID https://orcid.org/0000-0002-7310-2556
Sen XuSchool of Information and Science Technology, Zhejiang Shuren University, Hangzhou 310015, Zhejiang, China.ORCID https://orcid.org/0000-0002-4246-9320
Ting WangCollege of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, Jiangsu, China.ORCID https://orcid.org/0000-0001-6084-3243
Zhangquan WangSchool of Information and Science Technology, Zhejiang Shuren University, Hangzhou 310015, Zhejiang, China.ORCID https://orcid.org/0000-0002-8176-7494
Tiaojuan RenSchool of Information and Science Technology, Zhejiang Shuren University, Hangzhou 310015, Zhejiang, China.ORCID https://orcid.org/0000-0001-7699-4038

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the gradual improvement of people's living standards, the production and drinking of all kinds of food is increasing. People's disease rate has increased compared with before, which leads to the increasing number of medical image processing. Traditional technology cannot meet most of the needs of medicine. At present, convolutional neural network (CNN) algorithm using chaotic recursive diagonal model has great advantages in medical image processing and has become an indispensable part of most hospitals. This paper briefly introduces the use of medical science and technology in recent years. The hybrid algorithm of CNN in chaotic recursive diagonal model is mainly used for technical research, and the application of this technology in medical image processing is analysed. The CNN algorithm is optimized by using chaotic recursive diagonal model. The results show that the chaotic recursive diagonal model can improve the structure of traditional neural network and improve the efficiency and accuracy of the original CNN algorithm. Then, the application research and comparison of medical image processing are performed according to CNN algorithm and optimized CNN algorithm. The experimental results show that the CNN algorithm optimized by chaotic recursive diagonal model can help medical image automatic processing and patient condition analysis.

Indexed as

Image Processing, Computer-AssistedNeural Networks, ComputerAlgorithmsHumans

Identifiers

PMID34539771
PMCPMC8445709

What OpenQuestion holds

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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.