An Efficient Supervised Learning Technique for Tumour Detection and Analysis from MR Image Data Set

Authors(3) :-Kshipra Singh, Prof. Umesh Kumar Lilhore, Prof. Nitin Agrawal

Image mining plays a vital role in image analysis. It is a sub field of data mining technique and mainly focuses on knowledge discovery from image data sets. An Image mining technique uses mainly three major steps, image segmentation, detection and finally extraction of of information. In medical field image analysis for a medical image set such as MRI image data set, are always challenging for the researchers because medical image mining needs more accuracy in the mining results. Existing image mining methods encounters with several issues such as poor accuracy, higher detection time, and inaccurate tumour growth rate. In this research work we are presenting an efficient supervised learning method for tumour detection and analysis from MR image dataset. Proposed supervised learning method uses hybrid method. Initial it uses existing BWT method for data pre-processing and segmentation than later apply SVM+ PCA with object labelling method to extract final results for tumour image such as tumour size, type, growth rate. Existing BWT method with SVM and proposed BWT with SVM+ PCA are implemented over simulator MATLAB and various performance measuring parameters are calculated and experimental results analysis clearly shows that proposed method performs outstanding over existing BWT with SVM tumour detection method.

Authors and Affiliations

Kshipra Singh
M. Tech. Research Scholar, NRI Institute of Information Science & Technology Bhopal, Madhya Pradesh ,India
Prof. Umesh Kumar Lilhore
Head PG, NRI Institute of Information Science & Technology Bhopal, Madhya Pradesh, India
Prof. Nitin Agrawal
Associate Professor, NRI Institute of Information Science & Technology Bhopal, Madhya Pradesh, India

MRI, BWT, SVM, Object labeling, PCA, Brain Tumour

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Publication Details

Published in : Volume 3 | Issue 3 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 108-115
Manuscript Number : CSEIT183313
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Kshipra Singh, Prof. Umesh Kumar Lilhore, Prof. Nitin Agrawal, "An Efficient Supervised Learning Technique for Tumour Detection and Analysis from MR Image Data Set", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.108-115, March-April.2018

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