{"ID":2878836,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.17223","arxiv_id":"2508.17223","title":"Deep Learning Architectures for Medical Image Denoising: A Comparative Study of CNN-DAE, CADTra, and DCMIEDNet","abstract":"Medical imaging modalities are inherently susceptible to noise contamination that degrades diagnostic utility and clinical assessment accuracy. This paper presents a comprehensive comparative evaluation of three state-of-the-art deep learning architectures for MRI brain image denoising: CNN-DAE, CADTra, and DCMIEDNet. We systematically evaluate these models across multiple Gaussian noise intensities ($σ= 10, 15, 25$) using the Figshare MRI Brain Dataset. Our experimental results demonstrate that DCMIEDNet achieves superior performance at lower noise levels, with PSNR values of $32.921 \\pm 2.350$ dB and $30.943 \\pm 2.339$ dB for $σ= 10$ and $15$ respectively. However, CADTra exhibits greater robustness under severe noise conditions ($σ= 25$), achieving the highest PSNR of $27.671 \\pm 2.091$ dB. All deep learning approaches significantly outperform traditional wavelet-based methods, with improvements ranging from 5-8 dB across tested conditions. This study establishes quantitative benchmarks for medical image denoising and provides insights into architecture-specific strengths for varying noise intensities.","short_abstract":"Medical imaging modalities are inherently susceptible to noise contamination that degrades diagnostic utility and clinical assessment accuracy. This paper presents a comprehensive comparative evaluation of three state-of-the-art deep learning architectures for MRI brain image denoising: CNN-DAE, CADTra, and DCMIEDNet....","url_abs":"https://arxiv.org/abs/2508.17223","url_pdf":"https://arxiv.org/pdf/2508.17223v1","authors":"[\"Asadullah Bin Rahman\",\"Masud Ibn Afjal\",\"Md. Abdulla Al Mamun\"]","published":"2025-08-24T06:26:27Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}