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2000
Volume 21, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Background

Spinal image denoising plays a vital role in the accurate diagnosis of disc herniation (DH).

Objective

Traditional denoising algorithms perform less due Limited Directional Selectivity problem and do not adequately capture directional information in pixels. Traditional algorithms' edge representation and texture details are insufficient for the earlier detection of DH. Limited Directional Selectivity leads to inaccurate diagnosis and classification of Disc Herniation (DH) stages. The DH stages are (i) Degeneration (ii) Prolapse (iii) Extrusion and (iv) Sequestration. Moreover, detection of DH size below 2mm using MR image is the major problem.

Methods

To solve the above problem, spinal cord MR images fed to the proposed Parrot optimization tuned Denoising Convolutional Neural Network (Po-DnCNN) algorithm for perspective enhancement of nucleus pulposus region in the spinal cord, vertebrae. The perspective enhancement of Spinal cord image led to the accurate classification of stages and earlier detection of DH by using the proposed Hippopotamus optimization- Fast Hybrid Vision Transformer (Ho–FastViT) algorithm. For this study, spinal cord MR images are obtained from the Grand Challenge website – SPIDER dataset.

Results

The proposed Po-DnCNN method and Ho-FastViT results are analysed quantitatively and qualitatively based on the edge, contrast, classification of the stage, and enhancement of the projected nucleus pulposus region in the spinal cord and vertebrae. The predicted DH results using the proposed method are compared with the manual Pfirrman Grade value of the spinal card method.

Conclusion

Proposed method is better than traditional methods for earlier detection of DH. Po-DnCNN and Ho-FastViat methods give high accuracy of about 98% and 97% compared to traditional methods.

© 2025 The Author(s). Published by Bentham Science Publishers.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2025-10-31

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Spinal Cord Image Denoising Using Dncnn Algorithm
Curr. Med. Imaging 21, e15734056337613 (2025); https://doi.org/10.2174/0115734056337613241209072322
/content/journals/cmir/10.2174/0115734056337613241209072322
/content/journals/cmir/10.2174/0115734056337613241209072322
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  • Article Type:     Research Article
Keyword(s): Denoising convolutional neural network (DnCNN); Discrete wavelet transform (DWT); MR spine image; Nucleus pulposus; Stationary wavelet transform (SWT); Vertebrae

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