DocumentCode :
2466300
Title :
Extension Neural Network Approach to Classification of Brain MRI
Author :
Wang, Chuin-Mu ; Wu, Ming-Ju ; Chen, Jian-Hong ; Yu, Cheng-Yi
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Chin-Yi Inst. of Technol., Taichung, Taiwan
fYear :
2009
fDate :
12-14 Sept. 2009
Firstpage :
515
Lastpage :
517
Abstract :
Magnetic resonance image (MRI) has been widely used for clinical applications in recent years. With the ability of scanning the same section by multiple frequencies, MRI makes it possible to generate several images on the same section. Despite of accessible abundant information, MRI also makes it more difficult to judge the location of every tissue. MRI will complicate the judgment due to strong noise. In order to resolve this problem, this paper endeavors to classify them via the help of extension neural network (ENN), This paper has to demonstrate the advantages of extension theory, statistical theory is considered as a judgment method, whereby obtaining experimental data of extension neural network and perceptron for subsequent comparison. It has proved that extension is superior to the other algorithms in terms of classification.
Keywords :
brain; image classification; magnetic resonance imaging; medical image processing; neural nets; patient treatment; ENN; brain MRI; clinical application; extension neural network approach; extension theory; image classification; magnetic resonance image; patient diagnosis; patient treatment; Artificial neural networks; Biological neural networks; Biomedical imaging; Diseases; Instruments; Magnetic resonance imaging; Medical diagnostic imaging; Pathology; Training data; X-ray imaging; Classification; Extension; MRI; Neural Network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Information Hiding and Multimedia Signal Processing, 2009. IIH-MSP '09. Fifth International Conference on
Conference_Location :
Kyoto
Print_ISBN :
978-1-4244-4717-6
Electronic_ISBN :
978-0-7695-3762-7
Type :
conf
DOI :
10.1109/IIH-MSP.2009.141
Filename :
5337564
Link To Document :
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