Open AccessBrain Tumor Classification using Convolution Neural Network and Size Estimation by Marker Based Watershed Segmentation
Author(s) -
Sathya Seelan K,
Arun Kumar R,
S. Saranya,
R. Deepika,
V. Divya
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f8967.038620
Subject(s) - computer science , segmentation , artificial intelligence , convolutional neural network , pattern recognition (psychology) , classifier (uml) , convolution (computer science) , artificial neural network , image segmentation , contextual image classification , computer vision , image (mathematics)
Brain tumor classification and segmentation in the medical field is still a challenging task. Because we cannot identify through our naked eyes. Even Though several algorithms and methods developed to segment the brain tumor still accuracy is needed .By the single level classification we may not obtain the accurate result. So we propose the CNN (Convolution Neural Network) classifier which contains several layers. The convolution neural network uses kernals.The classification here is used to find the brain tumors such as glioma,meningioma and pituitary .The classified image is segmented using the watershed algorithm which segments based on the intensity.The segmentation employs here is to find the size of the tumor.