Brain Tumor Classification using Convolution Neural Network and Size Estimation by Marker Based Watershed Segmentation
Sathesh Kumar K.1, Arun Kumar R.2, Saranya S.3, Deepika R.4, Divya V.5
1Sathesh Kumar K.*, Department of IT, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu India.
2Arun Kumar R., Department of IT, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu India.
3Saranya S., Department of IT, M.A.M. College of Engineering and Technology, Trichy, Tamilnadu India.
4Deepika R., Department of IT, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu India.
5Divya V., Department of IT, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu India.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3633-3637 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8967038620/2020©BEIESP | DOI: 10.35940/ijrte.F8967.038620
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Convolution Neural Network, Watershed segmentation, Glioma, Meningioma, Pituitary.
Scope of the Article: Classification.