Evidence map›Paper›PMID 36785667›Full record

ArticleBioMed research international2023

Applications of Neural Network-Based Plan-Cancer Method for Primary Diagnosis of Mesothelioma Cancer.

Dhiraj Kapila, Sarika Panwar, M K Mohan Maruga Raja, Tamal Mondal, Shaik Mohammad Rafi, Suryabhan Pratap Singh, Bhupendra Kumar

RetractedAbstract readRetracted Publication
In one paragraph

Article in BioMed research international, 2023. 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 It has been retracted, and should not be counted.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

7 authors.

Dhiraj KapilaDepartment of Computer Science & Engineering, Lovely Professional University, Phagwara, Punjab, India.ORCID https://orcid.org/0000-0001-8064-6298
Sarika PanwarDepartment of Electronics and Telecommunication Engineering, AISSMS's Institute of Information Technology, Pune, India.ORCID https://orcid.org/0000-0002-5303-8307
M K Mohan Maruga RajaParul Institute of Pharmacy & Research, Parul University, Vadodara, Gujarat, India.ORCID https://orcid.org/0000-0002-1158-8386
Tamal MondalDepartment of Botany, Hiralal Mazumdar Memorial College for Women, Dakshineswar, Kolkata 700035, India.ORCID https://orcid.org/0000-0001-5369-215X
Shaik Mohammad RafiDepartment of Computer Science and Engineering, Sri Mittapalli College of Engineering, Guntur, Andhra Pradesh, India.ORCID https://orcid.org/0000-0001-5337-571X
Suryabhan Pratap SinghInformation Technology, Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur, India.ORCID https://orcid.org/0000-0003-4456-2032
Bhupendra KumarDebre Tabor University, Debre Tabor, Ethiopia.ORCID https://orcid.org/0000-0002-9609-2311

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

"Malignant mesothelioma (MM)" is an uncommon although fatal form of cancer. The proper MM diagnosis is crucial for efficient therapy and has significant medicolegal implications. Asbestos is a carcinogenic material that poses a health risk to humans. One of the most severe types of cancer induced by asbestos is "malignant mesothelioma." Prolonged shortness of breath and continuous pain are the most typical symptoms of the condition. The importance of early treatment and diagnosis cannot be overstated. The combination "epithelial/mesenchymal appearance of MM," however, makes a definite diagnosis difficult. This study is aimed at developing a deep learning system for medical diagnosis MM automatically. Otherwise, the sickness might cause patients to succumb to death in a short amount of time. Various forms of artificial intelligence algorithms for successful "Malignant Mesothelioma illness" identification are explored in this research. In relation to the concept of traditional machine learning, the techniques support "Vector Machine, Neural Network, and Decision Tree" are chosen. SPSS has been used to analyze the result regarding the applications of Neural Network helps to diagnose MM.

Indexed as

AsbestosMesotheliomaMesothelioma, MalignantArtificial IntelligenceHumansNeural Networks, ComputerAsbestos

Identifiers

PMID36785667
PMCPMC9922178

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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.