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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/6917
Title: A dual neural network ensemble approach for multiclass brain tumor classification
Authors: Sachdeva J.
Kumar V.
Gupta, Indra
Khandelwal N.
Ahuja C.K.
Published in: International Journal for Numerical Methods in Biomedical Engineering
Abstract: The present study is conducted to develop an interactive computer aided diagnosis (CAD) system for assisting radiologists in multiclass classification of brain tumors. In this paper, primary brain tumors such as astrocytoma, glioblastoma multiforme, childhood tumor-medulloblastoma, meningioma and secondary tumor-metastases along with normal regions are classified by a dual level neural network ensemble. Two hundred eighteen texture and intensity features are extracted from 856 segmented regions of interest (SROIs) and are taken as input. PCA is used for reduction of dimensionality of the feature space. The study is performed on a diversified dataset of 428 post contrast T1-weighted magnetic resonance images of 55 patients. Two sets of experiments are performed. In the first experiment, random selection is used which may allow SROIs from the same patient having similar characteristics to appear in both training and testing simultaneously. In the second experiment, not even a single SROI from the same patient is common during training and testing. In the first experiment, it is observed that the dual level neural network ensemble has enhanced the overall accuracy to 95.85% compared with 91.97% of single level artificial neural network. The proposed method delivers high accuracy for each class. The accuracy obtained for each class is: astrocytoma 96.29%, glioblastoma multiforme 96.15%, childhood tumor-medulloblastoma 90%, meningioma 93.00%, secondary tumor-metastases 96.67% and normal regions 97.41%. This study reveals that dual level neural network ensemble provides better results than the single level artificial neural network. In the second experiment, overall classification accuracy of 90.4% was achieved. The generalization ability of this approach can be tested by analyzing larger datasets. The extensive training will also further improve the performance of the proposed dual network ensemble. Quantitative results obtained from the proposed method will assist the radiologist in forming a better decision for classifying brain tumors. © 2012 John Wiley & Sons, Ltd.
Citation: International Journal for Numerical Methods in Biomedical Engineering (2012), 28(11): 1107-1120
URI: https://doi.org/10.1002/cnm.2481
http://repository.iitr.ac.in/handle/123456789/6917
Issue Date: 2012
Keywords: Brain tumor classification
Dual neural network ensemble
Feature extraction
Principal component analysis (PCA)
Segmented regions-of-interest (SROIs)
ISSN: 20407939
Author Scopus IDs: 54999212000
25646515800
56211916300
7006950739
23048455900
Author Affiliations: Sachdeva, J., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Kumar, V., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Gupta, I., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Khandelwal, N., Department of Radio Diagnosis, Post Graduate Institute of Medical Education and Research, Chandigarh, India
Ahuja, C.K., Department of Radio Diagnosis, Post Graduate Institute of Medical Education and Research, Chandigarh, India
Corresponding Author: Sachdeva, J.; Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India; email: jainysachdeva@gmail.com
Appears in Collections:Journal Publications [EE]

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