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Support Vector Machine for Classification of Spinal Cord Tumor

Geetha R.*, Mohan J.**

* ** Department of Electronics and Communication Engineering, SRM Valliammai Engineering College, SRM Nagar, Tamil Nadu.

Periodicity:January - March'2020
DOI : https://doi.org/10.26634/jip.7.1.16741

Abstract

This paper is a development of an algorithm to classify the spinal cord tumor in MRI images based on medical image processing. Most of researches involve deep learning algorithms to solve classification problem and is approached to detect tumor. From these considerations, approach of SVM for spinal cord tumor classification has been proposed. The method is improving the accuracy, sensitivity, specificity for the given spinal cord image. In pre-processing method, the spinal cord image is denoised using NLM filter, After image is denoised, Convolutional Neural Network(CNN) has been applied to extract the features from the segmented spinal cord image, which is obtained the texture details and used to identified the matched image. Support Vector Machine (SVM) is used for image classification. Finally, our proposed system is applied with a total of 500 images, including four kinds of spinal cord tumor images and our research experiment provides maximum value of the average accuracy, sensitivity, specificity are 98.1 (Astrocytomas), 98.9 (Astrocytomas), 97.17 (Hemangioblastomas) respectively. Minimum values of the accuracy, sensitivity, specificity are 93.8 (Ependymomas), 95.9 (Hemangioblastomas), 95.04 (Meningiomas) respectively.

Keywords

Spinal Cord Tumor, Types of Spinal Cord Tumor, Classification, Convolutional Neural Network, Support Vector Machine.

How to Cite this Article?

Geetha, R., and Mohan, J. (2020). Support Vector Machine for Classification of Spinal Cord Tumor. i-manager's Journal on Image Processing , 7(1), 40-44. https://doi.org/10.26634/jip.7.1.16741

References

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