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Erschienen in:
Digital Image Watermarking Using Sine Transform Technique Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Intervertebral Disc Classification Using Deep Learning Technique

verfasst von : J. V. Shinde, Y. V. Joshi, R. R. Manthalkar

Erschienen in: Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB)

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Low-Power High-Speed Hybrid Multiplier Architectures for Image Processing Applications
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Metadaten
Titel
Intervertebral Disc Classification Using Deep Learning Technique
verfasst von
J. V. Shinde
Y. V. Joshi
R. R. Manthalkar
Copyright-Jahr
2019
Buch
Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB)

Print ISBN: 978-3-030-00664-8

Electronic ISBN: 978-3-030-00665-5

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-00665-5_55

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