Abstract
Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ) is amongst the potential. The main goal of this paper is to enhance the performance of LVQ technique in order to gain higher accuracy detection for brain tumor in MRIs. The classical way of selecting the winner code vector in LVQ is to measure the distance between the input vector and the codebook vectors using Euclidean distance function. In order to improve the winner selection technique, round off function is employed along with the Euclidean distance function. Moreover, in competitive learning classifiers, the fitting model is highly dependent on the class distribution. Therefore this paper proposed a multiresampling technique for which better class distribution can be achieved. This multiresampling is executed by using random selection via preclassification. The test data sample used are the brain tumor magnetic resonance images collected from Universiti Kebangsaan Malaysia Medical Center and UCI benchmark data sets. Comparative studies showed that the proposed methods with promising results are LVQ1, Multipass LVQ, Hierarchical LVQ, Multilayer Perceptron, and Radial Basis Function.
| Original language | English |
|---|---|
| Article number | 8603609 |
| Journal | Computational and Mathematical Methods in Medicine |
| Volume | 2016 |
| DOIs | |
| Publication status | Published - 2016 |
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ASJC Scopus subject areas
- Applied Mathematics
- Modelling and Simulation
- Biochemistry, Genetics and Molecular Biology(all)
- Medicine(all)
- Immunology and Microbiology(all)
Cite this
Round Randomized Learning Vector Quantization for Brain Tumor Imaging. / Sheikh Abdullah, Siti Norul Huda; Bohani, Farah Aqilah; Nayef, Baher H.; Sahran, Shahnorbanun; Al Akash, Omar; Iqbal Hussain, Rizuana; Ismail, Fuad.
In: Computational and Mathematical Methods in Medicine, Vol. 2016, 8603609, 2016.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Round Randomized Learning Vector Quantization for Brain Tumor Imaging
AU - Sheikh Abdullah, Siti Norul Huda
AU - Bohani, Farah Aqilah
AU - Nayef, Baher H.
AU - Sahran, Shahnorbanun
AU - Al Akash, Omar
AU - Iqbal Hussain, Rizuana
AU - Ismail, Fuad
PY - 2016
Y1 - 2016
N2 - Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ) is amongst the potential. The main goal of this paper is to enhance the performance of LVQ technique in order to gain higher accuracy detection for brain tumor in MRIs. The classical way of selecting the winner code vector in LVQ is to measure the distance between the input vector and the codebook vectors using Euclidean distance function. In order to improve the winner selection technique, round off function is employed along with the Euclidean distance function. Moreover, in competitive learning classifiers, the fitting model is highly dependent on the class distribution. Therefore this paper proposed a multiresampling technique for which better class distribution can be achieved. This multiresampling is executed by using random selection via preclassification. The test data sample used are the brain tumor magnetic resonance images collected from Universiti Kebangsaan Malaysia Medical Center and UCI benchmark data sets. Comparative studies showed that the proposed methods with promising results are LVQ1, Multipass LVQ, Hierarchical LVQ, Multilayer Perceptron, and Radial Basis Function.
AB - Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ) is amongst the potential. The main goal of this paper is to enhance the performance of LVQ technique in order to gain higher accuracy detection for brain tumor in MRIs. The classical way of selecting the winner code vector in LVQ is to measure the distance between the input vector and the codebook vectors using Euclidean distance function. In order to improve the winner selection technique, round off function is employed along with the Euclidean distance function. Moreover, in competitive learning classifiers, the fitting model is highly dependent on the class distribution. Therefore this paper proposed a multiresampling technique for which better class distribution can be achieved. This multiresampling is executed by using random selection via preclassification. The test data sample used are the brain tumor magnetic resonance images collected from Universiti Kebangsaan Malaysia Medical Center and UCI benchmark data sets. Comparative studies showed that the proposed methods with promising results are LVQ1, Multipass LVQ, Hierarchical LVQ, Multilayer Perceptron, and Radial Basis Function.
UR - http://www.scopus.com/inward/record.url?scp=84982812645&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84982812645&partnerID=8YFLogxK
U2 - 10.1155/2016/8603609
DO - 10.1155/2016/8603609
M3 - Article
C2 - 27516807
AN - SCOPUS:84982812645
VL - 2016
JO - Computational and Mathematical Methods in Medicine
JF - Computational and Mathematical Methods in Medicine
SN - 1748-670X
M1 - 8603609
ER -