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Detection of brain tumor in MRI images, using combination of fuzzy c-means and SVM

Parveen, Singh, Amritpal·2015·2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)

Abstract

MRI is the most important technique, in detecting the brain tumor. In this paper data mining methods are used for classification of MRI images. A new hybrid technique based on the support vector machine (SVM) and fuzzy c-means for brain tumor classification is proposed. The purposed algorithm is a combination of support vector machine (SVM) and fuzzy c-means, a hybrid technique for prediction of brain tumor. In this algorithm the image is enhanced using enhancement techniques such as contrast improvement, and mid-range stretch. Double thresholding and morphological operations are used for skull striping. Fuzzy c-means (FCM) clustering is used for the segmentation of the image to detect the suspicious region in brain MRI image. Grey level run length matrix (GLRLM) is used for extraction of feature from the brain image, after which SVM technique is applied to classify the brain MRI images, which provide accurate and more effective result for classification of brain MRI images.

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Metadata

Authors
Parveen, & Singh, A.
Venue
2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)
Publisher
IEEE
Published
2015-02
Pages
98-102
OpenAlex
W2065196932
Cited by
210
References
0
FWCI
11.76
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Detection of brain tumor in MRI images, using combination of fuzzy c-means and SVM. https://app.cochat.ai/papers/10.1109/spin.2015.7095308
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Parveen, & Singh, A. (2015). Detection of brain tumor in MRI images, using combination of fuzzy c-means and SVM. 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN). https://doi.org/10.1109/spin.2015.7095308

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