Classification of the images of gene expression patterns using neural networks based on multi-valued neurons with the minimal number of inputs

Igor N. Aizenberg; Constantine Butakoff; Ekaterina Myasnikova; Maria Samsonova; John Reinitz

doi:10.1117/12.461675

5 April 2002 Classification of the images of gene expression patterns using neural networks based on multivalued neurons with the minimal number of inputs

Igor N. Aizenberg, Constantine Butakoff, Ekaterina Myasnikova, Maria Samsonova, John Reinitz

Author Affiliations +

Proceedings Volume 4668, Applications of Artificial Neural Networks in Image Processing VII; (2002) https://doi.org/10.1117/12.461675
Event: Electronic Imaging, 2002, San Jose, California, United States

Abstract

Multi-valued neurons (MVN) are the neural processing elements with complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partial-defined multiple-valued function on the single MVN. The MVN-based neural networks are applied to temporal classification of images of gene expression patterns, obtained by confocal scanning microscopy. The classification results confirmed the efficiency of this method for image recognition. It was shown that frequency domain of the representation of gene expression images is highly effective for their description.

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Igor N. Aizenberg, Constantine Butakoff, Ekaterina Myasnikova, Maria Samsonova, and John Reinitz "Classification of the images of gene expression patterns using neural networks based on multivalued neurons with the minimal number of inputs", Proc. SPIE 4668, Applications of Artificial Neural Networks in Image Processing VII, (5 April 2002); https://doi.org/10.1117/12.461675

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