Denoising and Segmentation of Medical Images Using N2S-U-Net

Authors

  • G Ashwini  Department of Electronics and Communication Engineering SV University, Tirupati, India
  • Dr. T. Ramashri  Department of Electronics and Communication Engineering SV University, Tirupati, India

Keywords:

Medical Imaging; Image Denoising; N2S-U-Net; Image Segmentation; Deep Learning

Abstract

Medical image processing faces significant chal- lenges, particularly in the denoising and segmentation of images where noise can severely degrade quality and affect diagnostic accuracy. This study presents a novel hybrid approach combining N2S-U-Net for image denoising with an enhanced U-Net architec- ture for segmentation. The N2S-U-Net model effectively denoises medical images without the need for paired clean images, en- hancing the subsequent segmentation process. Our approach has been evaluated on multiple imaging modalities, including chest X- rays (CXR), computed tomography (CT), and microscopy images. Results demonstrate substantial improvements in segmentation performance compared to conventional methods, underscoring the potential of this integrated approach to enhance diagnostic accuracy across diverse medical imaging modalities.

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International Journal of Scientific Research in Science and Technology

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Published

2023-12-30

Issue

Volume 10, Issue 6, November-December-2023

Section

Research Articles

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How to Cite

[1]
G Ashwini, Dr. T. Ramashri "Denoising and Segmentation of Medical Images Using N2S-U-Net" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 10, Issue 6, pp.586-594, November-December-2023.