Brain Tumour Detection using Convolutional Neural Network
P.V. Rama Raju1, G. Bharga Manjari2, G. Nagaraju3
1P.V. Rama Raju, Department of Electronics and Communication Engineering, SRKR Engineering College, Bhimavaram (Andhra Pradesh), India.
2G.B. Manjari, Department of Electronics and Communication Engineering, SRKR Engineering College, Bhimavaram (Andhra Pradesh), India.
3G. Nagaraju, Department of Electronics and Communication Engineering, SRKR Engineering College, Bhimavaram (Andhra Pradesh), India.
Manuscript received on 11 May 2019 | Revised Manuscript received on 05 June 2019 | Manuscript Published on 15 June 2019 | PP: 73-76 | Volume-8 Issue-1S3 June 2019 | Retrieval Number: A10140681S319/2019©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: In the field of human health care computer vision is playing an important role. The use of mainframe perception techniques in health care has one of the aim to decrease manual understanding in identification. Consequently manual error in understanding might be decreased. Brain associated diagnosis demands more care and a period of error in judgement might be harmful. Medical imaging is very important field in brain related diagnosis. More secured information about brain tissues provides by Magnetic Resonance Imaging. This paper presents an automatic segmentation technique based on convolution neural network, patch, analyzing 10*10 kernels using matlab. The main use of CNN’s their accuracy in image detection problems. Input image is changed into a specific number of patches for easy processing.
Keywords: Convolution Neural Network, Patch, Kernel, Brain Tumour, MRI.
Scope of the Article: Neural Information Processing