Automated cell phenotype image classification combining different methods

In this paper our aim is to study how an ensemble of classifiers can improve the performance of a machine learning technique for cell phenotype image classification. We want to point out some of the advantages that an ensemble of classifiers permits to obtain respect a stand-alone method. Finally, the preliminary results on the 2D-HeLa dataset, obtained by the fusion between a random subspace of Levenberg-Marquardt neural networks and a variant of the AdaBoost, are reported. It is interesting to note that the proposed system obtains an outstanding 97.5% Rank-1 accuracy and a >99% Rank-2 accuracy.