A Genetically Optimized Artificial Neural Network Structure for Feature Extraction and Classification of Vascular Tissue Fluorescence Spectrums

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Fifth IEEE International Workshop on Computer Architectures for Machine Perception (CAMP'00)

Padova, Italy

September 11-September 13

ISBN: 0-7695-0740-9

	ASCII Text	x
	G. Rovithakis, M. Maniadakis, M. Zervakis, "A Genetically Optimized Artificial Neural Network Structure for Feature Extraction and Classification of Vascular Tissue Fluorescence Spectrums," Computer Architectures for Machine Perception, International Workshop on, pp. 107, Fifth IEEE International Workshop on Computer Architectures for Machine Perception (CAMP'00), 2000.

	BibTex	x
	@article{ 10.1109/CAMP.2000.875964, author = {G. Rovithakis and M. Maniadakis and M. Zervakis}, title = {A Genetically Optimized Artificial Neural Network Structure for Feature Extraction and Classification of Vascular Tissue Fluorescence Spectrums}, journal ={Computer Architectures for Machine Perception, International Workshop on}, volume = {0}, year = {2000}, isbn = {0-7695-0740-9}, pages = {107}, doi = {http://doi.ieeecomputersociety.org/10.1109/CAMP.2000.875964}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Computer Architectures for Machine Perception, International Workshop on TI - A Genetically Optimized Artificial Neural Network Structure for Feature Extraction and Classification of Vascular Tissue Fluorescence Spectrums SN - 0-7695-0740-9 SP EP A1 - G. Rovithakis, A1 - M. Maniadakis, A1 - M. Zervakis, PY - 2000 KW - feature extraction; artificial neural network; feature extraction; classification; vascular tissue fluorescence spectrums; Genetic Algorithms; High Order Neural Networks; Multi-Layer Perceptron VL - 0 JA - Computer Architectures for Machine Perception, International Workshop on ER -

G. Rovithakis, Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece

M. Maniadakis, Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece

M. Zervakis, Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CAMP.2000.875964

The optimization of Neural Network structures for feature extraction and classification by employing Genetic Algorithms is addressed here. More precisely, a non-linear filter based on High Order Neural Networks (HONN) whose weights are updated by stable learning laws is used to extract the characteristic features of fluorescence spectrums correspond to human tissue samples of different stares. The process is optimized by a generic algorithm which maximizes the separability of different classes. The features are then classified with a Multi-Layer Perceptron (MLP). The high rates of success together with the small time needed to analyze the signals, proves our method very attractive for real time applications.

Index Terms:

feature extraction; artificial neural network; feature extraction; classification; vascular tissue fluorescence spectrums; Genetic Algorithms; High Order Neural Networks; Multi-Layer Perceptron

Citation:

G. Rovithakis, M. Maniadakis, M. Zervakis, "A Genetically Optimized Artificial Neural Network Structure for Feature Extraction and Classification of Vascular Tissue Fluorescence Spectrums," camp, pp.107, Fifth IEEE International Workshop on Computer Architectures for Machine Perception (CAMP'00), 2000

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