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Classification of Brain tumor using texture features with segmentation & FFNN approach: A Synopsis

File Size:
1.03 MB
Volume:
Volume 3, Issue 5 (May, 2017)
Publication No:
IJTC201705002
Author:
Parul Verma, Jasmeet Singh Gurm
Downloads:
4 x

Abstract:
Image segmentation is the process for extraction of valuable information from the hidden layers available in the image. In the process of image segmentation various approaches had been used for extraction of hidden information that can’t see through naked eyes. Image segmentation has a great significance in medical image sciences. In medical images gray scale images have been captured by different MRI, CT and X-ray equipment’s. Brain Tumor is effective cause of death of numerous patients. To reduce patient’s death rate various approaches had been made that can be used for detection of brain tumors using MRI images. Image segmentation is an effective tool to do so, but tumors can’t be easily extracted by performing segmentation on the images. To overcome these problem image segmentation have to be done on the basis of threshold value and neural network that has been feed forward use a set of input and output biases for extraction of best patterns.

Keywords:
Segmentation, Brain Tumor, Feed forward Neural Network.