Fast automated cell phenotype image classification

Ensemble of Neural Networks for Automated Cell Phenotype Image Classification

Subcellular location is related to the knowledge of the spatial distribution of a protein within the cell. The knowledge of the location of all proteins is crucial for several applications ranging from early diagnosis of a disease to monitoring of therapeutic effectiveness of drugs. This chapter focuses on the study of machine learning techniques for cell phenotype image classification and is a...

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 o...

A reliable method for cell phenotype image classification

OBJECTIVE Image-based approaches have proven to be of great utility in the automated cell phenotype classification, it is very important to develop a method that efficiently quantifies, distinguishes and classifies sub-cellular images. METHODS AND MATERIALS In this work, the invariant locally binary patterns (LBP) are applied, for the first time, to the classification of protein sub-cellular ...

Fast Automated Unpacking and Classification of Malware

Fast -NN Classification for Multichannel Image Data

A new fast and exact algorithm for determining the -NN classification of multichannel image data, and a new distance transform algorithm are described. Complexity analysis and empirical studies with magnetic resonance images (MRI) demonstrate the effectiveness of the new classification algorithm.