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CORRECTION article

Front. Med., 07 January 2025
Sec. Pulmonary Medicine

Corrigendum: Abnormal respiratory sounds classification using deep CNN through artificial noise addition

  • 1Faculty of Computing and Information Technology, University of Gujrat, Gujrat, Pakistan
  • 2Department of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah, Saudi Arabia
  • 3Independent Researcher, Bradford, United Kingdom
  • 4Cloud Computing and Applications Reseach Lab, Staffordshire University, Stoke-on-Trent, United Kingdom
  • 5Department of Computer and Network Engineering, College of Information Technology, UAE University, Al Ain, United Arab Emirates

A Corrigendum on
Abnormal respiratory sounds classification using deep CNN through artificial noise addition

by Zulfiqar, R., Majeed, F., Irfan, R., Rauf H. T., Benkhelifa, E., and Belkacem, A. N. (2021). Front. Med. 8:714811. doi: 10.3389/fmed.2021.714811

In the published article, there was an error in affiliation(s) [3]. Instead of “[Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent, United Kingdom]”, it should be “[Independent Researcher, Bradford, United Kingdom]”.

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: respiratory sounds, abnormal respiratory sounds, continuous adventitious sounds (CAS), discontinuous adventitious sounds (DAS), deep CNN

Citation: Zulfiqar R, Majeed F, Irfan R, Rauf HT, Benkhelifa E and Belkacem AN (2025) Corrigendum: Abnormal respiratory sounds classification using deep CNN through artificial noise addition. Front. Med. 11:1545847. doi: 10.3389/fmed.2024.1545847

Received: 15 December 2024; Accepted: 16 December 2024;
Published: 07 January 2025.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2025 Zulfiqar, Majeed, Irfan, Rauf, Benkhelifa and Belkacem. 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: Abdelkader Nasreddine Belkacem, YmVsa2FjZW0mI3gwMDA0MDt1YWV1LmFjLmFl

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.