Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network
Data(s) |
01/01/2006
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Resumo |
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. |
Formato |
application/pdf |
Identificador |
http://eprints.iisc.ernet.in/30664/1/sdarticle.pdf 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. |
Publicador |
Elsevier Science |
Relação |
http://dx.doi.org/10.1016/j.bspc.2006.05.002 http://eprints.iisc.ernet.in/30664/ |
Palavras-Chave | #Supercomputer Education & Research Centre |
Tipo |
Journal Article PeerReviewed |