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A Real-Time Approach to Brain Tumor Detection Implementing Wavelets and ANN

G Vijay Kumar1 , G V Raju2

Section:Review Paper, Product Type: Journal Paper
Volume-3 , Issue-11 , Page no. 89-93, Nov-2015

Online published on Nov 30, 2015

Copyright © G Vijay Kumar , G V Raju . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: G Vijay Kumar , G V Raju, “A Real-Time Approach to Brain Tumor Detection Implementing Wavelets and ANN,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.89-93, 2015.

MLA Style Citation: G Vijay Kumar , G V Raju "A Real-Time Approach to Brain Tumor Detection Implementing Wavelets and ANN." International Journal of Computer Sciences and Engineering 3.11 (2015): 89-93.

APA Style Citation: G Vijay Kumar , G V Raju, (2015). A Real-Time Approach to Brain Tumor Detection Implementing Wavelets and ANN. International Journal of Computer Sciences and Engineering, 3(11), 89-93.

BibTex Style Citation:
@article{Kumar_2015,
author = {G Vijay Kumar , G V Raju},
title = {A Real-Time Approach to Brain Tumor Detection Implementing Wavelets and ANN},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {11 2015},
volume = {3},
Issue = {11},
month = {11},
year = {2015},
issn = {2347-2693},
pages = {89-93},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=732},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=732
TI - A Real-Time Approach to Brain Tumor Detection Implementing Wavelets and ANN
T2 - International Journal of Computer Sciences and Engineering
AU - G Vijay Kumar , G V Raju
PY - 2015
DA - 2015/11/30
PB - IJCSE, Indore, INDIA
SP - 89-93
IS - 11
VL - 3
SN - 2347-2693
ER -

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Abstract

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique for assessing tumor detection. The need to differentiate between normal and abnormal tissues and determine type of abnormality before biopsy or surgery motivated development and application of fMRI. There are several technical reasons that make the brain easier than other organs to be examined with fMRI. This paper presents our proposed methods and results for the analysis of the brain spectra of patients with three tumor types. After extracting features from fMRI data using wavelet and wavelet packets, artificial neural networks are used to determine the abnormalities in the Tumor and the type. The proposed methods like clinical and simulated fMRI data and biopsy results. The fMRI analysis results were correct 97% of the time when classifying the spectra of the clinical fMRI data into normal tissue, tumor. and radiation necrosis.

Key-Words / Index Term

Magnetic Resonance Spectroscopic Imaging, Wavelet, Wavelet Packets, Artificial Neural Networks, Tumor, Necrosis

References

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