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CORRECTION article

Front. Med., 29 October 2025

Sec. Precision Medicine

Volume 12 - 2025 | https://doi.org/10.3389/fmed.2025.1724427

This article is part of the Research TopicImmune-Related Biomarkers in Skin and Breast Cancer: Innovations in Immunological Diagnostics and TherapiesView all 9 articles

Correction: An explainable hybrid deep learning framework for precise skin lesion segmentation and multi-class classification

This article is a correction to:

  1. An explainable hybrid deep learning framework for precise skin lesion segmentation and multi-class classification

    1. Read original article
Muhammad Fiaz,Muhammad Fiaz1,2Muhammad Bilal Shoaib KhanMuhammad Bilal Shoaib Khan1Abdul Hannan KhanAbdul Hannan Khan1Anas BilalAnas Bilal3Monir AbdullahMonir Abdullah4Abdulbasit A. Darem,Abdulbasit A. Darem5,6Raheem Sarwar Raheem Sarwar7*
  • 1Department of Computer Science, Green International University, Lahore, Pakistan
  • 2Department of Software Engineering, University of Central Punjab, Lahore, Pakistan
  • 3College of Information Science and Technology, Hainan Normal University, Haikou, China
  • 4Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha, Saudi Arabia
  • 5Center for Scientific Research and Entrepreneurship, Northern Border University, Arar, Saudi Arabia
  • 6Department of Computer Science, College of Science, Northern Border University, Arar, Saudi Arabia
  • 7OTEHM, Manchester Metropolitan University, Manchester, United Kingdom

A Correction on
An explainable hybrid deep learning framework for precise skin lesion segmentation and multi-class classification

by Fiaz, M., Shoaib Khan, M. B., Khan, A. H., Bilal, A., Abdullah, M., Darem, A. A., and Sarwar, R. (2025). Front. Med. 12:1681542. doi: 10.3389/fmed.2025.1681542

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.”

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: 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

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, ci5zYXJ3YXJAbW11LmFjLnVr

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.