Paper Info
Title
A GA-optimized Neural Network for Classification of Biological Particles from Electron-Microscopy Images
Abstract
Automatic classification of electron-microscopy images is an important step in the complex task of determination of the structure of biologial macromolecules. The process of 3D reconstruction from the images implies its previous classification in different classes corresponding to the main different views. In this paper a neural network classification algorithm has been used to perform the classification of electron microscopy samples in two classes. Using two labeled sets as a refference, the parameters and architecture of the classifier were optimized using a genetic algorithm. The global automatic process of training and optimization is implemented using the previously described g-lvq algorithm, and compared to a non-optimized version of the algorithm, Kohonen's LVQ. Using a part of the sample as training set, the results presented here show an efficient (90%) classification of unknown samples in two classes. The implication of this kind of automatic classification algorithms in determination of three dimensional structure of biological particles is finaly discused.
Year
DOI
Venue
1997
10.1007/BFb0032577
IWANN
Keywords
Field
DocType
electron-microscopy images,ga-optimized neural network,biological particles,3d reconstruction,electron microscopy,genetic algorithm,neural network
Pattern recognition,Computer science,Learning vector quantization,Self-organizing map,Probabilistic neural network,Artificial intelligence,Statistical classification,Artificial neural network,Classifier (linguistics),Genetic algorithm,Machine learning,3D reconstruction
Conference
Volume
ISSN
ISBN
1240
0302-9743
3-540-63047-3
Citations 
PageRank 
References 
1
0.47
6
Authors
5
Name
Order
Citations
PageRank
Juan J. Merelo Guervós1794128.38
Alberto Prieto279397.13
Federico Morán34511.19
Roberto Marabini45310.17
José María Carazo565456.25