Research Article
Classification of brain tumor using a multistage approach based on RELM and MLBP
@ARTICLE{10.4108/eetpht.v8i4.3082, author={Mrs. R. Bhavani and Dr. K. Vasanth}, title={Classification of brain tumor using a multistage approach based on RELM and MLBP}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={8}, number={4}, publisher={EAI}, journal_a={PHAT}, year={2022}, month={6}, keywords={multistage neighbouring, modified local binary pattern, regularized extreme learning machine}, doi={10.4108/eetpht.v8i4.3082} }- Mrs. R. Bhavani
Dr. K. Vasanth
Year: 2022
Classification of brain tumor using a multistage approach based on RELM and MLBP
PHAT
EAI
DOI: 10.4108/eetpht.v8i4.3082
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.
Copyright © 2022 R. Bhavani et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the originalwork is properly cited.
