Evidence map›Paper›PMID 37362712›Full record

ArticleMultimedia tools and applications2024

RETRACTED ARTICLE: COVID-19 Detection using adopted convolutional neural networks and high-performance computing.

Anil Kumar Singh, Ankit Kumar, Vinay Kumar, Shiv Prakash

RetractedAbstract readRetracted Publication
In one paragraph

Article in Multimedia tools and applications, 2024. 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 It has been retracted, and should not be counted.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

4 authors.

Anil Kumar SinghDepartment of Computer Science and Engineering, Pranveer Singh Institute of Technology, Dr. APJ Abdul Kalam Technical University, Lucknow, India.
Ankit KumarPranveer Singh Institute of Technology, Dr. APJ Abdul Kalam Technical University, Lucknow, India.
Vinay KumarDr. APJ Abdul Kalam Technical University, Lucknow, India.
Shiv PrakashDepartment of Electronics and Communication, University of Allahabad, Prayagraj, Uttar Pradesh India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID 19 pandemic is highly contagious disease is wreaking havoc on people's health and well-being around the world. Radiological imaging with chest radiography is one among the key screening procedure. This disease contaminates the respiratory system and impacts the alveoli, which are small air sacs in the lungs. Several artificial intelligence (AI)-based method to detect COVID-19 have been introduced. The recognition of disease patients using features and variation in chest radiography images was demonstrated using this model. In proposed paper presents a model, a deep convolutional neural network (CNN) with ResNet50 configuration, that really is freely-available and accessible to the common people for detecting this infection from chest radiography scans. The introduced model is capable of recognizing coronavirus diseases from CT scan images that identifies the real time condition of covid-19 patients. Furthermore, the database is capable of tracking detected patients and maintaining their database for increasing accuracy of the training model. The proposed model gives approximately 97% accuracy in determining the above-mentioned results related to covid-19 disease by employing the combination of adopted-CNN and ResNet50 algorithms.

Indexed as

Chest radiography picturesCNNComputed tomography (CT) scan Covid-19Polymerase chain responseResNet101ResNet50

Identifiers

PMID37362712
PMCPMC10199285

What OpenQuestion holds

Textmetadata
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.