Novel features for automated cell phenotype image classification.
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.
Nanni
Loris
L
Computer Information Systems, Missouri State University, MO 65804, USA.
Brahnam
Sheryl
S
Lumini
Alessandra
A
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D002477:Cells / Q000378:metabolism*; D019295:Computational Biology; D016208:Databases, Factual; D006367:HeLa Cells; D006801:Humans; D007091:Image Processing, Computer-Assisted; D051379:Mice; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D010641:Phenotype; D011506:Proteins / Q000378:metabolism
D011506:Proteins