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Related Experiment Video

Updated: Apr 22, 2026

Transtubular Endoscopic Posterolateral Decompression for L5-S1 Lumbar Lateral Disc Herniation
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Transtubular Endoscopic Posterolateral Decompression for L5-S1 Lumbar Lateral Disc Herniation

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Lumbar spine disorder detection using segmentation and IBN-MGNet classification with enhanced feature extraction.

Katepogu Surendra1, B Eswara Reddy1

  • 1Department of Computer Science and Engineering, JNTUA College of Engineering, Ananthapuramu, Andhra Pradesh, India.

The International Journal of Neuroscience
|April 20, 2026
PubMed
Summary

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This study introduces a novel deep learning framework for detecting lumbar spine disorders (LSDs), achieving high accuracy. The system refines image analysis for more reliable diagnosis of back pain causes.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Spine Disorders

Background:

  • Back pain is a prevalent global health issue, significantly contributed to by lumbar spine disorders (LSDs).
  • Accurate detection of LSDs is crucial for effective patient management and treatment.

Purpose of the Study:

  • To develop an advanced deep learning framework for enhanced detection of lumbar spine disorders (LSDs).
  • To improve the precision and reliability of LSD detection systems.

Main Methods:

  • A novel pipeline involving bilateral filtering for preprocessing.
  • Image segmentation using Proposed Residual Block with Patch-based RESU-NET (PRB-PRESU-NET).
  • Feature extraction combining Gaussian Filter with Scharr Operator-based Gradient Local Ternary Patterns (GSO-GLTP), MT, and deep features.
Keywords:
GSO-GLTPIBN-MGNetLumber spine disorderPRB-PRESU-NETbilateral filtering

Related Experiment Videos

Last Updated: Apr 22, 2026

Transtubular Endoscopic Posterolateral Decompression for L5-S1 Lumbar Lateral Disc Herniation
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Published on: October 14, 2022

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  • Classification utilizing the Improved Bottle Neck-based Modified GhostNet (IBN-MGNet).

    • Main Results:

    • The proposed framework achieved a high classification accuracy of 0.9312 for LSD detection.
    • The Scharr operator demonstrated superior edge detection capabilities compared to Sobel filters.
    • The developed system outperformed existing methods in precision and reliability.

    Conclusions:

    • The integrated approach of refined segmentation, feature extraction, and classification offers a reliable system for detecting lumbar spine disorders.
    • This deep learning framework represents a significant advancement in the diagnostic tools for LSDs.