Open Access

ARTICLE

SNCDM: Spinal Tumor Detection from MRI Images Using Optimized Super-Pixel Segmentation

T. Merlin Inbamalar^1,*, Dhandapani Samiappan², R. Ramesh³

1 Department of Electronics and Instrumentation Engineering, Saveetha Engineering College, Chennai, Tamilnadu, India
2 Department of Electronics and Communication Engineering, Saveetha Engineering College, Chennai,Tamilnadu, India
3 Department of Electronics and Communication Engineering, Tagore Engineering College, Chennai, Tamilnadu, India

* Corresponding Author: T. Merlin Inbamalar. Email:

Intelligent Automation & Soft Computing 2023, 36(2), 1899-1913. https://doi.org/10.32604/iasc.2023.031202

Received 12 April 2022; Accepted 12 May 2022; Issue published 05 January 2023

Abstract

Conferring to the American Association of Neurological Surgeons (AANS) survey, 85% to 99% of people are affected by spinal cord tumors. The symptoms are varied depending on the tumor’s location and size. Up-to-the-minute, back pain is one of the essential symptoms, but it does not have a specific symptom to recognize at the earlier stage. Numerous significant research studies have been conducted to improve spine tumor recognition accuracy. Nevertheless, the traditional systems are consuming high time to extract the specific region and features. Improper identification of the tumor region affects the predictive tumor rate and causes the maximum error-classification problem. Consequently, in this work, Super-pixel analytics Numerical Characteristics Disintegration Model (SNCDM) is used to segment the tumor affected region. Estimating the super-pixels of the affected region by this method reduces the variance between the identified pixels. Further, the super-pixels are selected according to the optimized convolution network that effectively extracts the vertebral super-pixels features. Derived super-pixels improve the network learning and training process, which minimizes the maximum error classification problem also the efficiency of the system was evaluated using experimental results and analysis.

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Keywords

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