CDSE-UNet: Enhancing COVID-19 CT Image Segmentation With Canny Edge Detection and Dual-Path SENet Feature Fusion
Accurate segmentation of COVID-19 CT images is crucial for reducing the severity and mortality rates associated with COVID-19 infections. In response to blurred boundaries and high variability characteristic of lesion areas in COVID-19 CT images, we introduce CDSE-UNet: a novel UNet-based segmentati...
Saved in:
| Main Authors: | Jiao Ding, Jie Chang, Renrui Han, Li Yang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
|
| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/ijbi/9175473 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
EFFECTIVENESS EDGE DETECTION OPERATOR CANNY TO IMPROVE IMAGE QUALITY THORAX CT SCAN IN CASES COVID-19
by: Prima Selvia Megawati, et al.
Published: (2023-11-01) -
CNN-SENet: a GNSS-R ocean wind speed retrieval model integrating CNN and SENet attention mechanism
by: Yimin Xia, et al.
Published: (2025-06-01) -
An optimize canny algorithm with traditional machine learning for edge detection enhancement
by: Russel Lafta, et al.
Published: (2025-04-01) -
Dual-Path Adversarial Denoising Network Based on UNet
by: Jinchi Yu, et al.
Published: (2025-08-01) -
Enchancing Lung Disease Classification through K-Means Clustering, Chan-Vese Segmentation, and Canny Edge Detection on X-Ray Segmented Images
by: Joko Riyono, et al.
Published: (2024-05-01)