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DOI: 10.14569/IJACSA.2019.0100466
PDF

Efficient MRI Segmentation and Detection of Brain Tumor using Convolutional Neural Network

Author 1: Alpana Jijja
Author 2: Dr. Dinesh Rai

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 4, 2019.

  • Abstract and Keywords
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Abstract: Brain tumor is one of the most life-threatening diseases at its advance stages. Hence, detection at early stages is very crucial in treatment for improvement of the life expectancy of the patients. magnetic resonance imaging (MRI) is being used extensively nowadays for detection of brain tumors that requires segmenting huge volumes of 3D MRI images which is very challenging if done manually. Thus, automatic segmentation of the images will significantly lessen the burden and also improve the process of diagnosing the tumors. This paper presents an efficient method based on convolutional neural networks (CNN) for the automatic segmentation and detection of a brain tumor using MRI images. Water cycle algorithm is applied to CNN to obtain an optimal solution. The developed technique has an accuracy of 98.5%.

Keywords: Brain tumor; segmentation; convolutional neural network; water cycle algorithm

Alpana Jijja and Dr. Dinesh Rai, “Efficient MRI Segmentation and Detection of Brain Tumor using Convolutional Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 10(4), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100466

@article{Jijja2019,
title = {Efficient MRI Segmentation and Detection of Brain Tumor using Convolutional Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100466},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100466},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {4},
author = {Alpana Jijja and Dr. Dinesh Rai}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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