This paper is published in Volume-6, Issue-1, 2020
Area
Electronics and Communication Engineering
Author
Shakeel Ahmad Mir
Co-authors
Sunil K. Panjeta
Org/Univ
Yamuna Institute of Engineering and Technology, Yamunanagar, Haryana, India
Pub. Date
18 March, 2020
Paper ID
V6I1-1301
Publisher
Keywords
Brain, Optimization, ANN, CNN

Citationsacebook

IEEE
Shakeel Ahmad Mir, Sunil K. Panjeta. Review of brain tumor classification by texture base optimize features, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Shakeel Ahmad Mir, Sunil K. Panjeta (2020). Review of brain tumor classification by texture base optimize features. International Journal of Advance Research, Ideas and Innovations in Technology, 6(1) www.IJARIIT.com.

MLA
Shakeel Ahmad Mir, Sunil K. Panjeta. "Review of brain tumor classification by texture base optimize features." International Journal of Advance Research, Ideas and Innovations in Technology 6.1 (2020). www.IJARIIT.com.

Abstract

Generally, experts perform certain evaluation procedures over these medical images which leads to operational errors. These errors require huge amount of time for making evaluations. MR images are qualitatively and quantitatively analysed by experts as per their experience. But it is limited to human eye, which is restricted up to 8 bits of grey level. Edge detection has its own reputation in image processing. Region boundaries and edges are quite similar, there is an iterative alteration of intensity at the region boundaries. Edge detection techniques recognized the edges which are non-continuous. There is a requirement of closed region boundaries, to segment an object from an image. The expected edges are the boundaries which lie between objects. It means edge based detection supports image segmentation.