Abstract
Mobile, fast virus detection and classification is of increasing importance in times of epidemic diseases being spread by global traveling and transport. A possible solution is the PAMONO sensor, an optical biological sensor that is able to detect (nanometer-sized) viruses and virus-like particles, utilizing surface plasmon resonance. Captured sensor data is given as image sequences, which can be analyzed by methods from the field of image processing, which is the focus this work. We classify single particles based on their size, using state of the art machine learning techniques, namely convolutional neural networks. This classification allows the measurement of individual particle sizes and the compilation of particle size distributions for a given suspension, which contributes to the goal of classifying different virus types. The classification procedure and estimation of distributions is evaluated using real PAMONO sensor image sequences and particles that simulate viruses. The results show that informative features of the SPR signals can be automatically learned, extracted and used for classification, successfully.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer-Verlag GmbH Deutschland
About this paper
Cite this paper
Lenssen, J.E., Shpacovitch, V., Weichert, F. (2017). Real-Time Virus Size Classification Using Surface Plasmon PAMONO Resonance and Convolutional Neural Networks. In: Maier-Hein, geb. Fritzsche, K., Deserno, geb. Lehmann, T., Handels, H., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2017. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54345-0_26
Download citation
DOI: https://doi.org/10.1007/978-3-662-54345-0_26
Published:
Publisher Name: Springer Vieweg, Berlin, Heidelberg
Print ISBN: 978-3-662-54344-3
Online ISBN: 978-3-662-54345-0
eBook Packages: Computer Science and Engineering (German Language)