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.