Indian Journal of Public Health Research & Development
Year : 2019, Volume : 10, Issue : 2
First page : ( 1097) Last page : ( 1102)
Print ISSN : 0976-0245. Online ISSN : 0976-5506.
Article DOI : 10.5958/0976-5506.2019.00444.3

Detection of Brain Tumor using Back Propagation Algorithm through MRI

Sridevi A.¹, Sabitha T.²

¹Associate Professor, M. Kumarasamy College of Engineering (Autonomous), Karur

²PG student, M. Kumarasamy College of Engineering (Autonomous), Karur

Online published on 15 March, 2019.

Abstract

Radiology is a vast subject and requires more knowledge and understanding for exact diagnosis of tumor in medical science. In this work, a meningioma segment and detection approach is designed using MRI sequence images as input image for defining the tumor point. This experiments is a difficult to the large diversification in the existence of tumor tissues related to various inmate and most of the cases similarity within the normal tissues makes the task difficult. The main impartial is to categorize the brain into the presence meningioma or a healthy brain. proposed system has following main steps, Edge based Contourlet Transformation for registration process carried out as pre-processing step, next segmentation of tumor point using region-expanding segmentation, for aspect extraction step, two types of texture features are combined Otsus Thresholding, k-means and Local Binary markings texture aspect for efficient meningioma detection and finally for classification adopting neural network methods is imported out. The proposed approach implements a novel procedure which uses a back propagation detection of meningioma from Slices scan image. The combination of GLRLM and CS-LBP features outperformed well in discriminating between normal and affected tumor tissue and also for classification a back propagation algorithm are used and attained the better classification accuracy. The proposed algorithm is implemented using Mat lab, for various MRI sequences the experimental results are obtained for Image Registration and segmentation using point of growing. The Segmented images are corelate with victims data base and classify it as severe or Benign using ANN classifier.

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Keywords

Merciless tumor, benign tumor, backpropagation, otsus thresholding, k means clustering.

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