Optimal Res-UNET architecture with deep supervision for tumor segmentation.

Journal: Frontiers in medicine
Published Date: May 30, 2025

Abstract

BACKGROUND: Brain tumor segmentation is critical in medical imaging due to its significance in accurate diagnosis and treatment planning. Deep learning (DL) methods, particularly the U-Net architecture, have demonstrated considerable promise. However, optimizing U-Net variants to enhance performance and computational efficiency remains challenging.

Authors

  • Rahman Maqsood
    Department of Information Systems, University of Management and Technology, Lahore, Pakistan.
  • Fazeel Abid
    School of Information Science and Technology, Northwest University, Xi'an 710127, China.
  • Jawad Rasheed
    Department of Computer Engineering, Istanbul Sabahattin Zaim University, 34303, Istanbul, Turkey. jawadrasheed@ieee.org.
  • Onur Osman
    Department of Electrical and Electronics Engineering, Istanbul Topkapi University, Istanbul, Türkiye.
  • Shtwai Alsubai
    Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals