k-NN Based Classifcation of Brain MRI Images using DWT and PCA to Detect Different Types of Brain Tumour
Journal Title: International Journal of Medical Research & Health Sciences (IJMRHS) - Year 2017, Vol 6, Issue 9
Abstract
The treatment of any tumour is completely depended what type it is. This paper proposes a set of algorithms which work for the better detection and classifcation of brain tumour. The MRI image based brain tumour analysis would effciently deal with classifcation process for brain tumour analysis. The proposal suggests using three stages namely feature extraction, feature reduction and classifcation. The discrete wavelet transformation methods (DWT) and principal component analysis (PCA) are used for feature extraction and feature reduction respectively. The k-Nearest Neighbours (k-NN) classifer is used for the classifcation of the tumour and to tell what type the tumour is Benign or Malignant. By using different methods for feature extraction, feature reduction and classifcation techniques to extract and calculate the average values of the features of the MRI image and to match the values to the values extracted from the set of input image and classify them to be benign or malignant.
Authors and Affiliations
Ayush Arora| Student, Department of Computer Science and Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India Corresponding e-mail: aayusharora6896@gmail.com, Priyanshu Roy| Student, Department of Computer Science and Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India, Shwetha MD| Professor, Department of Computer Science and Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India, S Venktesan| Professor, Department of Computer Science and Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India, Ramesh Babu| Professor, Department of Computer Science and Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India
Keywords
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