MRI Brain Tumor Locating using Neural Networks

Pavithra R*
Department of Electronics and Communication Engineering, Government College of Engineering, Tirunelveli, Tamil Nadu, India.
Periodicity:January - March'2020
DOI : https://doi.org/10.26634/jip.7.1.17098

Abstract

Brain tumor identification is difficult task in the early stage of life. But now it has been improved with various machine learning algorithms. Now-a-days issue of brain tumor automatic identification is of great curious. In Order to discover the brain tumor of a patient, the data like MRI images of a patient's brain is used. Here our problem is to identify whether tumor is present or not in patients brain. It is very important to detect the tumors at starting level for a healthy life of patient. There are many literatures on discovering these kinds of brain tumors and improving the detection accuracies. In this paper, we estimate the brain tumor seriousness using Convolutional Neural Network(CNN) algorithm, which gives us accurate results.

Keywords

Convolutional Neural Network, Tumor Detection, Wiener Filter.

How to Cite this Article?

Pavithra, R. (2020). MRI Brain Tumor Locating using Neural Networks. i-manager's Journal on Image Processing , 7(1), 35-39. https://doi.org/10.26634/jip.7.1.17098

References

(1). Eleyan, A. (2017). Simple and novel approach for image representation with application to face recognition. International Journal of Intelligent Systems and Applications in Engineering, 5(3), 89-93.
(2). Eleyan, A., & Demirel, H. (2011). Co-occurrence matrix and its statistical features as a new approach for face recognition. Turkish Journal of Electrical Engineering & Computer Sciences, 19(1), 97-107. https://doi.org/ 10.3906/elk-0906-27
(3). Giraddi, S., Pujari, J., & Seeri, S. (2015). Role of GLCM features in identifying abnormalities in the retinal images. International Journal of Image, Graphics and Signal Processing, 7(6), 45-51. https://doi.org/10.5815/ ijigsp.2015.06.06
(4). Kaldera, H. N. T. K., Gunasekara, S. R., & Dissanayake, M. B. (2019, March). Brain tumor classification and segmentation using faster R-CNN. In 2019 Advances in Science and Engineering Technology International Conferences (ASET) (pp. 1-6). IEEE. https://doi.org/10.1109/ICASET.2019.8714263
(5). Mohanaiah, P., Sathyanarayana, P., & GuruKumar, L. (2013). Image texture feature extraction using GLCM approach. International Journal of Scientific and Research Publications, 3(5), 1-5.
(6). Mohsen, H., El-Dahshan, E. S. A., El-Horbaty, E. S. M., & Salem, A. B. M. (2018). Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal, 3(1), 68-71. https://doi.org/10.1016 /j.fcij.2017.12.001
(7). Othman, M. F. B., Abdullah, N. B., & Kamal, N. F. B. (2011, April). MRI brain classification using support vector machine. In 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization (pp. 1-4). IEEE. https://doi.org/10.1109/ICMSAO.2011.5775605
(8). Pathak, B., & Barooah, D. (2013). Texture analysis based on the gray-level co-occurrence matrix considering possible orientations. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(9), 4206-4212.
(9). Shree, N. V., & Kumar, T. N. R. (2018). Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Informatics, 5(1), 23-30. https://doi.org/10.10 07/s40708-017-0075-5
(10). Sompong, C., & Wongthanavasu, S. (2016, July). Brain tumor segmentation using cellular automata-based th fuzzy c-means. In 2016 13 International Joint Conference on Computer Science and Software Engineering (JCSSE) (pp. 1-6). IEEE. https://doi.org/ 10.1109/JCSSE.2016.7748902
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.