Open Access
CORRECTION
Correction: Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space
1 Department of Computer Engineering, Bahauddin Zakariya University, Multan, 60000, Pakistan
2 Department of Computer Science, Kansas State University, Manhattan, KS 66506, USA
3 Department of Computer Science, University of Management and Technology, Lahore, 54000, Pakistan
4 Independent Researcher, Bradford, BD8 0HS, UK
5 Industrial Engineering Department, Collage of Engineering, King Saud University, P.O. Box 800, Riyadh, 11421, Saudi Arabia
* Corresponding Authors: Muhammad Imran Sharif. Email: ; Hafiz Tayyab Rauf. Email:
Computers, Materials & Continua 2025, 82(1), 1461-1461. https://doi.org/10.32604/cmc.2024.061589
Issue published 03 January 2025
This article is a correction of:
Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space
Read original article
Abstract
This article has no abstract.In the article “Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space” by Mudassir Khalil, Muhammad Imran Sharif, Ahmed Naeem, Muhammad Umar Chaudhry, Hafiz Tayyab Rauf, Adham E. Ragab Computers, Materials & Continua, 2023, Vol. 77, No. 2, pp. 2031–2047. DOI: 10.32604/cmc.2023.043687, URL: https://www.techscience.com/cmc/v77n2/54831, there was an error regarding the affiliation for the author Hafiz Tayyab Rauf. Instead of “Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent, ST4 2DE, UK”, the affiliation should be “Independent Researcher, Bradford, BD8 0HS, UK”.
The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original article has been updated.
Please find below the corrected information:
This correction was approved by the Editors-in-Chief and the Editorial Office of Computers, Materials & Continua. The corrected version of the article has been updated accordingly.
Cite This Article
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.