Classification of brain tumor using a multistage approach based on RELM and MLBP

R. Bhavani; K. Vasanth

doi:10.4108/eetpht.v8i4.3082

Authors

R. Bhavani Research Scholar
K. Vasanth Professor/Electrical and Communication Engineering

DOI:

https://doi.org/10.4108/eetpht.v8i4.3082

Keywords:

multistage neighbouring, modified local binary pattern, regularized extreme learning machine

Abstract

INTRODUCTION: Automatic segmentation and classification of brain tumors help in improvement of treatment which will increase the life of the patient. Tumor may be noncancerous (benign) or cancerous (malignant). Precancerous cells may also form into cancer.

OBJECTIVES: Hough CNN is applied for selected section which applies hough casting technique in segmentation. METHODS: A multistage methodof extracting features, with multistage neighbouring is done for emerging an exact brain tumor classifying methodology.

RESULTS: In this dataset three types of brain tumors are available they are meningioma, glioma, and pituitary.. CONCLUSION: This paperpresented an efficient brain tumor classification approach which involves multiscale preprocessing, multiscale feature extraction and classification.

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