Arun Kumar
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Arun Kumar has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
Alaknanda Ashok
Women Institute of Technology, Uttarakhand Technical University, Dehradun 248001, Uttrakhand, India
1 shared publication- M. A. Ansari
Gautam Buddha University
1 shared publication - Pragati Tripathi
New Horizon College of Engineering
1 shared publication
Latest Publications
- 1 January 2020
- Published by Taru Publications in Journal of Interdisciplinary Mathematics
- Vol. 23 (5) , 967-976
Novel scheme of k-SVM analysis using PCA and NN for detection of MRI brain images
The MRI brain image detection and classification is very crucial for medical science. From last few years, various methods are being presented. Here in this paper, we proposed a novel approach to classify a given MRI brain image as normal or abnormal i.e. affected through tumor. The performed method firstly taken the wavelet transform for feature extraction from the images then applied principle component analysis to decrease the dimensions of features. Then the decreased features are being employed to kernel-support vector machine (k-SVM). Here the data has been taken for seven general brain diseases viz. glioma, Alzheimer's disease, Sarcoma, coma, LINN, IPOL, HPOL. We collected the data of 20 normal and 140 abnormal patients from Harvard Medical Laboratory website. Here four methods of SVM are performed such as L-Kernel, RBF, Poly, and NN. The comparative analysis is also being performed by the existing literatures which illustrates the result by DWT+PCA+KSVM by applying with GRB Kernel. Our classification gives fast and accurate results and assist doctors to distinguish between normal or abnormal to certain precision values.
- 1 January 2020
- Published by Taru Publications in Journal of Interdisciplinary Mathematics
- Vol. 23 (5) , 967-976
Novel scheme of k-SVM analysis using PCA and NN for detection of MRI brain images
The MRI brain image detection and classification is very crucial for medical science. From last few years, various methods are being presented. Here in this paper, we proposed a novel approach to classify a given MRI brain image as normal or abnormal i.e. affected through tumor. The performed method firstly taken the wavelet transform for feature extraction from the images then applied principle component analysis to decrease the dimensions of features. Then the decreased features are being employed to kernel-support vector machine (k-SVM). Here the data has been taken for seven general brain diseases viz. glioma, Alzheimer's disease, Sarcoma, coma, LINN, IPOL, HPOL. We collected the data of 20 normal and 140 abnormal patients from Harvard Medical Laboratory website. Here four methods of SVM are performed such as L-Kernel, RBF, Poly, and NN. The comparative analysis is also being performed by the existing literatures which illustrates the result by DWT+PCA+KSVM by applying with GRB Kernel. Our classification gives fast and accurate results and assist doctors to distinguish between normal or abnormal to certain precision values.