DSNET: A Lightweight Segmentation Model for Segmentation of Skin Cancer Lesion Regions
Currently, most skin disease segmentation tasks tend to use large models to achieve better segmentation performance. However, in real medical application scenarios, due to the limited computing and storage resources of hardware devices, it is difficult to deploy large models in business systems for...
Saved in:
| Main Authors: | Yucong Chen, Guang Yang, Xiaohua Dong, Junying Zeng, Chuanbo Qin |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10876159/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
UCM-NetV2: An efficient and accurate deep learning model for skin lesion segmentation
by: Chunyu Yuan, et al.
Published: (2025-11-01) -
LMFUNet: A Lightweight Multi-fusion UNet Based on Spiking Neural Systems for Skin Lesion Segmentation
by: Ningkang Hu, et al.
Published: (2024-01-01) -
Multi-Conv attention network for skin lesion image segmentation
by: Zexin Li, et al.
Published: (2024-12-01) -
Lung Segmentation with Lightweight Convolutional Attention Residual U-Net
by: Meftahul Jannat, et al.
Published: (2025-03-01) -
Segmentation of dermatoscopic images of skin lesions. Comparison of methods
by: A. F. Smalyuk, et al.
Published: (2024-05-01)