An enhanced denoising system for mammogram images using deep transformer model with fusion of local and global features
Abstract Image denoising is a critical problem in low-level computer vision, where the aim is to reconstruct a clean, noise-free image from a noisy input, such as a mammogram image. In recent years, deep learning, particularly convolutional neural networks (CNNs), has shown great success in various...
Saved in:
| Main Authors: | A. Robert Singh, Suganya Athisayamani, Faten Khalid Karim, Ahmed Zohair Ibrahim, Sameer Alshetewi, Samih M. Mostafa |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-89451-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Robust Hybrid CNN+ViT Framework for Breast Cancer Classification Using Mammogram Images
by: Vasudha Rani Patheda, et al.
Published: (2025-01-01) -
Mammograms in cosmetic breast surgery
by: Shiffman M
Published: (2005-01-01) -
Mammograms in cosmetic breast surgery
by: M A Shiffman
Published: (2005-07-01) -
Assessment of the Severity of Breast Artery Calcification on a Mammogram: Intraoperator and Interoperator Reproducibility
by: E. V. Bochkareva, et al.
Published: (2021-11-01) -
A novel diagnostic framework for breast cancer: Combining deep learning with mammogram-DBT feature fusion
by: Nishu Gupta, et al.
Published: (2025-03-01)