Chaplot, Sandeep and Patnaik, LM and Jagannathan, NR (2006) Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. In: Biomedical Signal Processing and Control, 1 (1). pp. 86-92.
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In this paper. we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% front support vector machine. We observed that the classification rate is high for a Support vector machine classifier compared to self-organizing map-based approach.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Elsevier Science.|
|Keywords:||Magnetic resonance imaging (MRI);Discrete wavelet transform (DWT);Artificial neural network (ANN);Self-organizing maps (SOM);Support vector machine (SVM)|
|Department/Centre:||Division of Information Sciences > Supercomputer Education & Research Centre|
|Date Deposited:||25 Aug 2010 06:58|
|Last Modified:||19 Sep 2010 06:12|
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