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Reseach Article

Brain Tumor Detection and Classification with Feed Forward Back-Prop Neural Network

by Neha Rani, Sharda Vashisth
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 12
Year of Publication: 2016
Authors: Neha Rani, Sharda Vashisth
10.5120/ijca2016910738

Neha Rani, Sharda Vashisth . Brain Tumor Detection and Classification with Feed Forward Back-Prop Neural Network. International Journal of Computer Applications. 146, 12 ( Jul 2016), 1-6. DOI=10.5120/ijca2016910738

@article{ 10.5120/ijca2016910738,
author = { Neha Rani, Sharda Vashisth },
title = { Brain Tumor Detection and Classification with Feed Forward Back-Prop Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 12 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number12/25447-2016910738/ },
doi = { 10.5120/ijca2016910738 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:50:13.004329+05:30
%A Neha Rani
%A Sharda Vashisth
%T Brain Tumor Detection and Classification with Feed Forward Back-Prop Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 12
%P 1-6
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Brain is an organ that controls activities of all the parts of the body. Recognition of automated brain tumor in Magnetic resonance imaging (MRI) is a difficult task due to complexity of size and location variability. This automatic method detects all the type of cancer present in the body. Previous methods for tumor are time consuming and less accurate. In the present work, statistical analysis morphological and thresholding techniques are used to process the images obtained by MRI. Feed-forward back-prop neural network is used to classify the performance of tumors part of the image. This method results high accuracy and less iterations detection which further reduces the consumption time.

References
  1. Al-Badarneh et al., H.Najadat. and I.Jordan, ``A classifier to detect MRI Brain Images’”, The ACM International Conference on Advances in Social Networks Analysis and Minning, pp.784-787, 2013.
  2. V. Amasaveni, and A. Singh, ``Detection of Brain tumor by using Neural Network”, The International Conference of Electronic Computer Technology, 2013.
  3. M. Avula, and Lakkhkula, et al., ``Bone Cancer from MRI Scan Imagery using Mean pixel intensity”, The International Conference of Electronic Computer Technology, pp, 112-116, 2014.
  4. Chan, et al., and Y.Gal, ``Automatic Detection of Arterial vowels in Dynamic Contrast Enhancement MR images of Brain”, The school of School of ITEF university of Queensland, Australia, pp-978-983, 2014.
  5. Chanet al. and Y. Gal,”Database,”bhttp: //www.med.harvard.edu/AANLIB/home.html”, 2011.
  6. Chudler, E. H,”Brain dataset: http://faculty.washington.edu/chudler/facts.html. visited on 18/3/2011.
  7. A.A. Constantin, and Berkeley et al., ``Unsupervised Segmentation of Brain Tissue in Multivariate MRI”, The Electrical Engineering and Computer Sciences University of California, Berkely, Vol.20, pp.89-92, 2011.
  8. Deepa, et al. and B.A. Devi, `` Neural Networks design for Classification of Brain Tumor”, The international Conference on Computer Communication and Informatics, Coimbatore, pp-568-573, 2012.
  9. S. N. Deepa, and B.A. Devi, ``Neural Network and SMO based Classification for Brain Tumor”, IEEE, pp-1032-1034, 2011.
  10. S. Ghanavati, J. Li,., and T. Liu, `` Automatic Brain Tumor Detection in Magnetic Resonance Images”, IEEE signal Ltd. Vol.24, pp.574-577, 2012.
  11. Ibrahim, et al. and A.A lrhman,``MRI Image Classification using Neural Network”, The International Conference on Computing, Electrical and Electronics Engineering’, Sudan, pp-253-258, 2011.
  12. K. Machhale, H. B Nandpuru. and V. Kapur, ``MRI Brain Cancer Classification Using Hybrid Classifier”, The International Conference on Industrial Instruments and Control, pp.60-65, 2015.
  13. D. Sridhar and M. Krishna ,``Brain TumorClassification Using Discrete Cosine Transform and Probabilistic Neural Network”, International Conference on Signal Processing, Image Processing and Pattern Recognition, pp.1-5. 2013.
  14. M. Surugavalli , ``Brain tumor Classification using two tier classifier with adaptive segmentationtechnique”, The Institute of Engineering andTechnology, Vol.10,pp.10-17, 2016.
  15. Takate, et al. and P. S. Vikhe , `` Classification ofMRI Brain Images using K-NN and K-means”, IEEE,pp.-55-58, 2012.
  16. S. Vashisth, M. Khan, R. Vijay and A. K .Salhan,” Online acquisition of wireless transmission carotid waveform transforms to analyze posture related changes”, International Journal of Biomedical Engineering and Technology, Vol. 10, No.3, 2012.
Index Terms

Computer Science
Information Sciences

Keywords

MRI Brain tumor Statistical Morphological Correlation Thresholding Feed-Forward backward network.