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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/17094
Title: Classification of brain tumors using PCA-ANN
Authors: Kumar V.
Sachdeva J.
Gupta, Indra
Khandelwal N.
Ahuja C.K.
Published in: Proceedings of 2011 World Congress on Information and Communication Technologies, WICT 2011
Abstract: The present study is conducted to assist radiologists in marking tumor boundaries and in decision making process for multiclass classification of brain tumors. Primary brain tumors and secondary brain tumors along with normal regions are segmented by Gradient Vector Flow (GVF)-a boundary based technique. GVF is a user interactive model for extracting tumor boundaries. These segmented regions of interest (ROIs) are than classified by using Principal Component Analysis-Artificial Neural Network (PCA-ANN) approach. The study is performed on diversified dataset of 856 ROIs from 428 post contrast T1- weighted MR images of 55 patients. 218 texture and intensity features are extracted from ROIs. PCA is used for reduction of dimensionality of the feature space. Six classes which include primary tumors such as Astrocytoma (AS), Glioblastoma Multiforme (GBM), child tumor-Medulloblastoma (MED) and Meningioma (MEN), secondary tumor-Metastatic (MET) along with normal regions (NR) are discriminated using ANN. Test results show that the PCA-ANN approach has enhanced the overall accuracy of ANN from 72.97 % to 95.37%. The proposed method has delivered a high accuracy for each class: AS-90.74%, GBM-88.46%, MED-85.00%, MEN-90.70%, MET-96.67%and NR-93.78%. It is observed that PCA-ANN provides better results than the existing methods. © 2011 IEEE.
Citation: Proceedings of 2011 World Congress on Information and Communication Technologies, WICT 2011, (2011), 1079- 1083. Mumbai
URI: https://doi.org/10.1109/WICT.2011.6141398
http://repository.iitr.ac.in/handle/123456789/17094
Issue Date: 2011
Keywords: brain tumor classification
feature extraction
Gradient Vector Flow (GVF)
Principal component analysis (PCA)
regions of interest (ROIs)
ISBN: 9.78147E+12
Author Scopus IDs: 25646515800
54999212000
56211916300
7006950739
23048455900
Author Affiliations: Kumar, V., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttrakhand, India
Sachdeva, J., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttrakhand, India
Gupta, I., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttrakhand, India
Khandelwal, N., Department of Radiodiagnosis, Post Graduate Institute of Medical Education and Research, Chandigarh, India
Ahuja, C.K., Department of Radiodiagnosis, Post Graduate Institute of Medical Education and Research, Chandigarh, India
Corresponding Author: Kumar, V.; Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttrakhand, India; email: vinodfee@iitr.ernet.in
Appears in Collections:Conference Publications [EE]

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