Optimal Res-UNET architecture with deep supervision for tumor segmentation

Optimal Res-UNET architecture with deep supervision for tumor segmentation

BackgroundBrain 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 a...

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Bibliographic Details
Main Authors: Rahman Maqsood, Fazeel Abid, Jawad Rasheed, Onur Osman, Shtwai Alsubai
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-05-01
Series:Frontiers in Medicine
Subjects:
magnetic resonance imaging
Residual U-Net
deep supervision
medical image analysis
dice loss
image segmentation challenges
Online Access:https://www.frontiersin.org/articles/10.3389/fmed.2025.1593016/full
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