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CORRECTION article

Front. Med.

Sec. Precision Medicine

Volume 12 - 2025 | doi: 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 3 articles

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

Provisionally accepted
Muhammad FiazMuhammad Fiaz1,2Bilal ShoaibBilal Shoaib1Abdul Hannan KhanAbdul Hannan Khan1Dr Anas BilalDr Anas Bilal3Monir Abdullah KaidMonir Abdullah Kaid4Abdulbasit DaremAbdulbasit Darem5Raheem SarwarRaheem Sarwar6*
  • 1Green International University, Lahore, Pakistan
  • 2University of Central Punjab, Lahore, Pakistan
  • 3Hainan Normal University, Haikou, China
  • 4University of Bisha, Bishah, Saudi Arabia
  • 5Northern Border University, Arar, Saudi Arabia
  • 6Manchester Metropolitan University, Manchester, United Kingdom

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

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Keywords: skin disease, Classification, segmentation, Explainable AI, Grad-CAM

Received: 14 Oct 2025; Accepted: 17 Oct 2025.

Copyright: © 2025 Fiaz, Shoaib, Khan, Bilal, Kaid, 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) or licensor 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.

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