Fusion of systems for automated cell phenotype image classification

Abstract

Automated cell phenotype image classification is related to the problem of determining locations of protein expression within living cells. Localization of proteins in cells is directly related to their functions and it is crucial for several applications ranging from early diagnosis of a disease to monitoring of therapeutic effectiveness of drugs. Recent advances in imaging instruments and biological reagents have allowed fluorescence microscopy to be extensively used as a tool to understand biology at the cellular level by means of the visualization of biological activity within cells. However, human classification of fluorescence cell micrographs is still subjective and very time consuming, thus an automated approach for the systematic determination of protein subcellular locations from fluorescence microscopy images is required. Existing approaches concentrated on designing a set of optimal features and then applying standard machine-learning algorithms. This paper takes into consideration the best methods proposed in the literature and focuses on the study of ensemble machine learning techniques for cell phenotype image classification. Two techniques are tested for the classification: a random subspace of Levenberg–Marquardt neural networks and a variant of the AdaBoost. Each of these two methods are tested with different feature sets, moreover the fusion between the two ensembles is studied. The best ensemble tested in this work obtains an outstanding 97.5% accuracy in the 2D-Hela dataset, which to the best of our knowledge is the best performance obtained on this dataset (the most used benchmark for comparing automated cell phenotype image classification approaches).