Advanced Brain Tumor Segmentation With a Multiscale CNN and Conditional Random Fields
The use of high-precision automatic algorithms for segmenting brain tumors has the potential to improve disease diagnosis, treatment monitoring, and large-scale pathological studies. In this study, we present a novel 9-layer multiscale architecture designed specifically for the semantic segmentation...
Saved in:
| Main Authors: | Ala Guennich, Mohamed Othmani, Hela Ltifi |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10883974/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
AG-MS3D-CNN multiscale attention guided 3D convolutional neural network for robust brain tumor segmentation across MRI protocols
by: Umesh Kumar Lilhore, et al.
Published: (2025-07-01) -
UnetTransCNN: integrating transformers with convolutional neural networks for enhanced medical image segmentation
by: Yi-Hang Xie, et al.
Published: (2025-07-01) -
3D-SCUMamba: An Abdominal Tumor Segmentation Model
by: Juwita, et al.
Published: (2025-01-01) -
ES-UNet: efficient 3D medical image segmentation with enhanced skip connections in 3D UNet
by: Minyoung Park, et al.
Published: (2025-08-01) -
CDA-mamba: cross-directional attention mamba for enhanced 3D medical image segmentation
by: Jiashu Xu, et al.
Published: (2025-07-01)