Paper Info
Title
A Neural Network Based Classification of Human Blood Cells in a Multiphysic Framework
Abstract
Living cells possess properties that enable them to withstand the physiological environment as well as mechanical stimuli occurring within and outside the body. Any deviation from these properties will undermine the physical integrity of the cells as well as their biological functions. Thus, a quantitative study in single cell mechanics needs to be conducted. In this paper we will examine fluid flow and Neo-Hookean deformation. Particularly, a mechanical model to describe the cellular adhesion with detachment is proposed. Restricting the interest on the contact surface and elaborating again the computational results, it is possible to develop our idea about to reproduce the phases coexistence in the adhesion strip. Subsequently, a number of simulations have been carried out, involving a number of human cells with different mechanical properties. All the collected data have been used in order to train and test a suitable Artificial Neural Network (ANN) in order to classify the kind of cell. Obtained results assure good performances of the implemented classifier, with very interesting applications.
Year
DOI
Venue
2008
10.1007/978-3-642-03040-6_88
Advances in Neuro-Information Processing
Keywords
Field
DocType
mechanical stimulus,single cell mechanic,biological function,cellular adhesion,different mechanical property,mechanical model,obtained result,human blood cells,neo-hookean deformation,neural network,adhesion strip,multiphysic framework,human cell,fluid flow,artificial neural network
Pattern recognition,Computer science,Cell mechanics,Adhesion,Cell adhesion,Fluid dynamics,Artificial intelligence,Classifier (linguistics),Artificial neural network
Conference
Volume
ISSN
Citations 
5507
0302-9743
0
PageRank 
References 
Authors
0.34
1
5
Name
Order
Citations
PageRank
Matteo Cacciola1149.10
Maurizio Fiasché2499.23
Giuseppe Megali36412.40
Francesco C. Morabito4175.46
Mario Versaci55115.70