Enhancing Diagnostic Precision: A Distribution-Based Compressed Denoising Scheme using Transfer Learning for Noise Reduction in Medical Imaging
The DCDS framework presents a promising, lightweight, adaptive, and accurate solution for medical image denoising. It holds potential for realtime diagnostic support, but further optimization and clinical validation are necessary to ensure generalizability across diverse imaging modalities and noise types.
Authors
- Shtwai Alsubai (ORCID: https://orcid.org/0000-0002-6584-7400)
- Assefa Senbato Genale (ORCID: https://orcid.org/0000-0002-7411-9456)
- Mohd Anjum (ORCID: https://orcid.org/0000-0003-4094-3786)
- Ashit Kumar Dutta (ORCID: https://orcid.org/0000-0002-1208-2678)
- Sana Shahab (ORCID: https://orcid.org/0000-0003-0254-3351)
- Waseem Ahmad (ORCID: https://orcid.org/0000-0002-1215-2920)
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- Alfaisal University (SA)
- Prince Sattam Bin Abdulaziz University (SA)
- Aligarh Muslim University (IN)
- Bule Hora University (ET)
- University of Business and Technology (SA)
- Almaarefa University
- Gautam Buddha University (IN)
Publication Details
- Journal
- Current Medical Imaging Formerly Current Medical Imaging Reviews
- Published
- 2025-09-19
- DOI
- https://doi.org/10.2174/0115734056391923250827170359
- Primary Topic
- Image and Signal Denoising Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00