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RETRACTION article

Front. Public Health, 03 January 2024
Sec. Digital Public Health
Volume 11 - 2023 | https://doi.org/10.3389/fpubh.2023.1358295
This article is part of the Research Topic Emerging Challenges for Deep Learning in Dealing with Biomedical and Healthcare Data View all 5 articles

Retraction: Deep fractional max pooling neural network for COVID-19 recognition

This article is a retraction of:

  1. RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

    1. Read original article
\r\nFrontiers Editorial Office Frontiers Editorial Office*

A Retraction of the Original Research Article
Deep fractional max pooling neural network for COVID-19 recognition

by Wang, S.-H., Satapathy, S. C., Anderson, D., Chen, S.-X., and Zhang, Y.-D. (2021). Front. Public Health 9:726144. doi: 10.3389/fpubh.2021.726144

The journal retracts the 10 August 2021 article cited above.

Following publication, the publisher uncovered evidence that false identities were used in the peer-review process. The assignment of fake reviewers was confirmed by an investigation, conducted in accordance with Frontiers' policies and the Committee on Publication Ethics (COPE) guidelines. Given the concerns, the editors no longer have confidence in the findings presented in the article. UPDATE (30 July 2024): This notice is to alert readers of this matter, it does not imply involvement of the co-authors.

This retraction was approved by the Chief Editors of Frontiers in Public Health and the Chief Executive Editor of Frontiers. The authors received a communication regarding the retraction and had a chance to respond. This communication has been recorded by the publisher.

Citation: Frontiers Editorial Office (2024) Retraction: Deep fractional max pooling neural network for COVID-19 recognition. Front. Public Health 11:1358295. doi: 10.3389/fpubh.2023.1358295

Received: 19 December 2023; Accepted: 21 December 2023;
Published: 03 January 2024.

Approved by:

Paolo Vineis, Imperial College London, United Kingdom

Copyright © 2024 Frontiers Editorial Office. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Frontiers Editorial Office, research.integrity@frontiersin.org

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