Round Randomized Learning Vector Quantization for Brain Tumor Imaging
Joint Authors
Sheikh Abdullah, Siti Norul Huda
Bohani, Farah Aqilah
Nayef, Baher H.
Sahran, Shahnorbanun
Al Akash, Omar
Iqbal Hussain, Rizuana
Ismail, Fuad
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-19, 19 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-07-18
Country of Publication
Egypt
No. of Pages
19
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Sheikh Abdullah, Siti Norul Huda& Bohani, Farah Aqilah& Nayef, Baher H.& Sahran, Shahnorbanun& Al Akash, Omar& Iqbal Hussain, Rizuana…[et al.]. 2016. Round Randomized Learning Vector Quantization for Brain Tumor Imaging. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-19.
https://search.emarefa.net/detail/BIM-1100213
Modern Language Association (MLA)
Sheikh Abdullah, Siti Norul Huda…[et al.]. Round Randomized Learning Vector Quantization for Brain Tumor Imaging. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-19.
https://search.emarefa.net/detail/BIM-1100213
American Medical Association (AMA)
Sheikh Abdullah, Siti Norul Huda& Bohani, Farah Aqilah& Nayef, Baher H.& Sahran, Shahnorbanun& Al Akash, Omar& Iqbal Hussain, Rizuana…[et al.]. Round Randomized Learning Vector Quantization for Brain Tumor Imaging. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-19.
https://search.emarefa.net/detail/BIM-1100213
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100213