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Intervertebral Disc Classification Using Deep Learning Technique

  • J. V. ShindeEmail author
  • Y. V. Joshi
  • R. R. Manthalkar
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

This paper describes the semiautomatic method for diagnosis of intervertebral disc degeneration according to Pfirrmann’s five scale (1–5) grading system, which is used in the assessment of disc degeneration severity. Total 1123 discs are obtained after augmentation from 120 subject’s T2-weighted lumbar scans. Manual classification into five grades is done by experts. Our method is extracting 59 features using Local Binary Pattern for texture analysis and 4096 features using pretrained CNN. 1 × 59 and 1 × 4096 feature vectors are fused to form 1 × 4155 feature vector to train our multiclass Support Vector Machine classifier. This feature level fusion method is able to achieve 80.40% accuracy. A Quantitative analysis is done using parameters, viz.,—Accuracy, Sensitivity, Specificity, Precision, Recall, F1 score, etc.

Keywords

Deep learning Intervertebral disc degeneration Classification 

Notes

Acknowledgements

We thank Dr. Hemant Borse, Consultant Radiologist, Samarth Diagnostic Center, Nasik and Dr. Rajesh Jawale, Consultant Radiologist, Wockhardt Hospital Nasik (M.S.) India who provided insight and expertise that greatly assisted the research. We are immensely grateful to Dr. Hemant Borse and his technical team for providing spine MR image dataset for research work.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • J. V. Shinde
    • 1
    Email author
  • Y. V. Joshi
    • 2
  • R. R. Manthalkar
    • 2
  1. 1.Department of Computer EngineeringL.G.N. Sapkal College of EngineeringNasikIndia
  2. 2.Department of Electronics & TelecommunicationS.G.G.S Institute of Engineering and TechnologyNandedIndia

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