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

Published: 11 September 2000 Publication History

Abstract

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

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        cover image Guide Proceedings
        CAMP '00: Proceedings of the Fifth IEEE International Workshop on Computer Architectures for Machine Perception (CAMP'00)
        September 2000
        ISBN:0769507409

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        IEEE Computer Society

        United States

        Publication History

        Published: 11 September 2000

        Author Tags

        1. Genetic Algorithms
        2. High Order Neural Networks
        3. Multi-Layer Perceptron
        4. artificial neural network
        5. classification
        6. feature extraction
        7. vascular tissue fluorescence spectrums

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