Cervical spondylosis detection using deep dense auxiliary inception network

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Chitte Pankaj Pramod; Gokhale Ulhaskumar; Kapur Vivek; Padole Dinesh

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Journal Paper

Paper Information

Journal: INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS Year:2021 | Volume:12 | Issue:Special Issue Page(s): 1595-1604

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Information Journal Paper

Title

Cervical spondylosis detection using deep dense auxiliary inception network

Author(s)

Chitte Pankaj Pramod | Gokhale Ulhaskumar | Kapur Vivek | Padole Dinesh | Issue Writer Certificate

Keywords

Cervical spine

Cervical spondylosis

Deep learning

X-Ray imaging

Inception

Abstract

Cervical spondylosis is a recurring spinal syndrome in which the spine progressively tightens and that can eventually become fully rigid. Early diagnosis is really an efficient way of improving the recovery rate and reducing costs. Due to the difficult and comprehensive procedure for recognizing Cervical spondylosis in the initial stages, this area is untreated. Strong correlations of the vertebrae make the automatic detection procedure challenging. These minor variations in the X-ray image make visual interpretation a challenging task involving skilled explorers. Even after this, the problem still remains untreated and also the feasibility of even an automatic detection framework has still not been addressed for this application. Thus, the Deep learning-based method was used to predict some potential relevance of Cervical spondylosis has. The proposed system can be used to detect the onset of Cervical spondylosis in the early stages using Deep learning techniques.

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