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

, Frontiers Editorial Office

doi:10.3389/fpubh.2023.1358295

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:

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

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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

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