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Paper Title
Brain Tumor Classification into Normal and Abnormal Using PCA and PNN Classifier
  Deven D. Ketkar,  Ashish B. Vartak

There are several number of diseases which are entering in human life due to modern lifestyle, unhealthy food. Also there is no control on fast food eatables. If medical field can take help of several technological aspects, then it will give fine results. Use of automated techniques can give efficient diagnosis. Considering one of the dangerous disease like brain tumor, accurate prediction about tumor malignancy is necessary. We are going to use several Magnetic Resonance Images (MRI’s) for training and testing purpose. Classification of tumor is done using two methods Principal Component Analysis (PCA) and Probabilistic Neural Network (PNN) classifier. PCA can be used for Feature extraction of MRI images. Features which we are going to calculate are Mean, Deviation and Eigen Vectors. We are going to take several number of MR images which includes Normal and abnormal images. Features mentioned above corresponding to these images are calculated and with the help of these features we are going to train PNN classifier. When test image comes at the input side, features corresponding to that image are calculated and compared with training dataset and based on similarity between the features, tumor classification is done.

Keywords- Magnetic Resonance Imaging (MRI), Principal Component Analysis (PCA), Probabilistic Neural Network (PNN), Mean, Deviation, Eigen Vectors, Euclidean Distance.
Publication Details
Unique Identification Number - IJEDR1601016
Page Number(s) - 97-102
Pubished in - Volume 4 | Issue 1 | January 2016
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
  Deven D. Ketkar,  Ashish B. Vartak,   "Brain Tumor Classification into Normal and Abnormal Using PCA and PNN Classifier", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.4, Issue 1, pp.97-102, January 2016, Available at :http://www.ijedr.org/papers/IJEDR1601016.pdf
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