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Cervical Diseases Prediction Using LHVR Techniques

Praveena Rajasekaran*, Preetha Jaganathan, Anjali Annadurai
Muthayammal Engineering College, Kakaveri, Rasipuram, 637408, India
* Corresponding Author: Praveena Rajasekaran. Email:

Computer Systems Science and Engineering 2021, 36(3), 477-484. https://doi.org/10.32604/csse.2021.014247

Received 08 September 2020; Accepted 02 December 2020; Issue published 18 January 2021

Abstract

The stabilizing mechanisms of cervical spine spondylosis are involved in the degenerating segmentation vertebra, which often causes pain. Health of the individual is affected, both physically and mentally. Due to depression, nervousness, and psychological damages occur thereby losing their human activity functions. The nucleus pulposus of spinal disc herniation is prolapsed through a deficiency of annulus fibrosus. A jelly-like core part of the disc contains proteins that cause the tissues to become swollen when it touches the nucleus pulposus. The proposed Gradient Linear Classification (GLC) algorithm is used for the efficient automatic classification of disc degeneration herniation of Inter vertebral/ vertebra in a cervical disc. Distance between the disc degeneration is classified through gradient operator and is estimated using the rotation of angles between the correlations. Specialists of the orthopedic spine are searching for high-precision algorithms, which are achieved using proposed Linear Hybrid Vertebra Regression (LHVR) diagnostic techniques to identify the degree of cervical disc degeneration using an accurate location. Our experimental results have been used to determine a high range of classification in predicting the spinal cord which saves handling time and accomplishes high accuracy in detection.

Keywords

Cervical diseases; GLC; LHVR; disc prediction; classify disc degeneration

Cite This Article

P. Rajasekaran, P. Jaganathan and A. Annadurai, "Cervical diseases prediction using lhvr techniques," Computer Systems Science and Engineering, vol. 36, no.3, pp. 477–484, 2021.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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