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Medical imaging techniques such as X-rays and Magnetic Resonance Imaging (MRI) are critical tools in clinical diagnostics. However, these images often suffer from various types of noise introduced during acquisition, transmission, or processing, which can hinder accurate interpretation and diagnosis. This project presents a deep learning-based denoising approach aimed at enhancing image clarity while preserving essential diagnostic features. The proposed method utilizes a convolutional neural network (CNN) architecture trained on paired noisy and clean images to learn complex mappings that suppress noise effectively. Special attention is given to preserving anatomical structures and pathological features critical to clinical assessment.
Keywords:
Convolutional Neural Networks (CNN), Deep Learning, Magnetic Resonance Imaging (MRI),Noise Reduction,Image Quality Improvement,Diagnostic Image Enhancement,Medical Image Denoising
Cite Article:
"Denoising Techniques for Medical Images: A Comparative Study on X-rays and MRIs", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 12, page no.a731-a735, December-2025, Available :http://www.ijrti.org/papers/IJRTI2512093.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator