In the published article, there was a mistake in the Acknowledgments. The project number for Northern Border University support was shown as “NBU-CRP-2025-2903”. The correct statement is:
“The authors are thankful to the Deanship of Graduate Studies and Scientific Research at the University of Bisha for supporting this work through the Fast-Track Research Support Program, and the authors extend their appreciation to Northern Border University, Saudi Arabia, for supporting this work through project number NBU-FFR-2025-2903-17.”
Statements
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.
Summary
Keywords
skin disease, classification, segmentation, explainable AI, Grad-CAM
Citation
Fiaz M, Shoaib Khan MB, Khan AH, Bilal A, Abdullah M, Darem AA and Sarwar R (2025) Correction: An explainable hybrid deep learning framework for precise skin lesion segmentation and multi-class classification. Front. Med. 12:1724427. doi: 10.3389/fmed.2025.1724427
Received
14 October 2025
Accepted
17 October 2025
Published
29 October 2025
Approved by
Frontiers Editorial Office, Frontiers Media SA, Switzerland
Volume
12 - 2025
Updates
Copyright
© 2025 Fiaz, Shoaib Khan, Khan, Bilal, Abdullah, Darem and Sarwar.
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: Raheem Sarwar r.sarwar@mmu.ac.uk
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.