Novel Features for Automated Cell Phenotype Image Classification

Conference paper

DOI: 10.1007/978-1-4419-5913-3_24

Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 680)
Cite this paper as:
Nanni L., Brahnam S., Lumini A. (2010) Novel Features for Automated Cell Phenotype Image Classification. In: Arabnia H. (eds) Advances in Computational Biology. Advances in Experimental Medicine and Biology, vol 680. Springer, New York, NY


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 the individual features that 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 subcellular localization databases.


Pattern classification and recognition Image processing in medicine and biological sciences 

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Computer Information SystemsMissouri State UniversitySpringfieldUSA

Personalised recommendations