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Published in:

Volume 5 Issue 5
May-2018
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR1805725


Registration ID:
182561

Page Number

739-744

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Title

Brain Tumor Detection and Classification from MRI using Machine Learning and Image Processing Novel Approach

Abstract

Brain tumor is currently a series problem for our world many people die because of abnormally growth of human red blood cell inside human skull. Detection and classification of brain tumor is too difficult because of the complex nature of brain. Now a day’s different modern medical imaging technology is used to capture the clear image of our brain including soft tissue. Magnetic resonances Image (MRI), Posterior Emission Tomography (PET), X-Ray CT-Scan are some of the popular one. Among them MRI becomes the most common and popular because of free-radiation. Traditionally brain tumor segmentation and classification is done by radiologist that is vulnerable and prune to error, Medical Image Analysis is now becomes a hot research area in the field of Engineering and Medical, many research and method is proposed and currently also going on. In this paper a novel approach is proposed to detect and classify brain tumor using combination of image processing and machine learning algorithms. This proposed method is valid for all types of image type including the original DICOM (Digital Imaging of communication for Medicine). Image processing is mainly used for pre-processing step including skull removal, after skull removal the data is feed to unsupervised machine learning algorithm combination of K-means and fuzzy c-means segmentation, the next step is to apply Wavelet transform for feature extraction followed by Principal component analysis to reduce the dimensionality, next we evaluate around 13 features of statistical measurement, Kernel Support vector machine is used for classification of the tumor being benign and being malignant, the area of the tumor is also calculated, maximum accuracy is achieved from Gaussian radial basis with percentage of 92.6 %.

Key Words

Tumor, Machine Learning, Kernel Support Vector Machine, PCA, DWT, DICOM, Benign, Malignant, Image Processing.

Cite This Article

"Brain Tumor Detection and Classification from MRI using Machine Learning and Image Processing Novel Approach ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 5, page no.739-744, MAY-2018, Available :http://www.jetir.org/papers/JETIR1805725.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Brain Tumor Detection and Classification from MRI using Machine Learning and Image Processing Novel Approach ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 5, page no. pp739-744, MAY-2018, Available at : http://www.jetir.org/papers/JETIR1805725.pdf

Publication Details

Published Paper ID: JETIR1805725
Registration ID: 182561
Published In: Volume 5 | Issue 5 | Year May-2018
DOI (Digital Object Identifier):
Page No: 739-744
Country: vadodara, gujarat, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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