Automatic segmentation of chest X-ray images via deep-improved various U-Net techniques
Objectives Accurate segmentation of medical images is vital for effective disease diagnosis and treatment planning. This is especially important in resource-constrained environments. This study aimed to evaluate the performance of various U-Net-based deep learning architectures for chest X-ray (CXR)...
Saved in:
| Main Authors: | Sedat Orenc, Mehmet Sirac Ozerdem, Emrullah Acar, Musa Yilmaz |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-08-01
|
| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251366855 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocks
by: Wiley Tam, et al.
Published: (2025-01-01) -
Automatic Calculation of Cardiometric Coefficients on Chest X-Ray Images
by: Alexey Kornaev, et al.
Published: (2025-01-01) -
Lightweight U-Net for Blood Vessels Segmentation in X-Ray Coronary Angiography
by: Jesus Salvador Ramos-Cortez, et al.
Published: (2025-03-01) -
Semantic Lung Segmentation from Chest X-ray Images Using Seg-Net Deep CNN Model
by: Dathar Abas Hasan, et al.
Published: (2023-10-01) -
A Hybrid Deep Learning Framework for Accurate Cell Segmentation in Whole Slide Images Using YOLOv11, StarDist, and SAM2
by: Julius Bamwenda, et al.
Published: (2025-06-01)