The most common method of handling automated cell phenotype image classification is to determine a common set of optimal features and then apply standard machine-learning algorithms to classify them. In this chapter we use advanced methods for determining a set of optimized features for training an ensemble using random subspace with a set of Levenberg-Marquardt neural networks. The process requires that we first run several experiments to determine which individual features offer the most information. The best performing features are then concatenated and used in the ensemble classification. Applying this approach we have obtained an average accuracy of 97.4% using the three best benchmarks for this problem: the 2D HeLa dataset and both the endogenous and the transfected LOCATE mouse protein sub-cellular localization databases.
Nanni, L., Brahnam, S., Lumini, A. (2010). Novel Features for Automated Cell Phenotype Image Classification. NEW YORK : Springer.
Novel Features for Automated Cell Phenotype Image Classification
NANNI, LORIS;LUMINI, ALESSANDRA
2010
Abstract
The most common method of handling automated cell phenotype image classification is to determine a common set of optimal features and then apply standard machine-learning algorithms to classify them. In this chapter we use advanced methods for determining a set of optimized features for training an ensemble using random subspace with a set of Levenberg-Marquardt neural networks. The process requires that we first run several experiments to determine which individual features offer the most information. The best performing features are then concatenated and used in the ensemble classification. Applying this approach we have obtained an average accuracy of 97.4% using the three best benchmarks for this problem: the 2D HeLa dataset and both the endogenous and the transfected LOCATE mouse protein sub-cellular localization databases.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
