International Journal of Electrical, Electronics and Data Communication (IJEEDC)
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Statistics report
Mar. 2020
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 85
Paper Published : 1402
No. of Authors : 3795
  Journal Paper

Paper Title
Naive Bayes Classifier for Brain Tumor Detection

Abstract
The brain tumor detection is the approach which can detect the tumor portion from the MRI image. To detect tumor from the image various techniques has been proposed in the previous times. The technique which is adapted in research work is based upon morphological scanning, Clustering and naïve Bayes classification. The morphological scanning will scan the input image and clustering will cluster similar and dissimilar patches from image then naïve Bayes classifier spot the tumor portion from magnetic resonance imaging. The advance algorithm is implemented in MATLAB and results are analyzed in terms of PSNR, MSE accuracy and fault detection and also calculate overlapping area with dice coef.The proposed method has been tested on data set with more than 25 slide scanned images. This proposed method achieved accuracy with 86% best cell detection. Keywords - Brain Tumor, Clustering, MRI, Morphological Scanning, Naive Bayes NBC-BTD model.


Author - Gurkarandesh Kaur, Ashish Oberoi

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| Published on 2019-12-19
   
   
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