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Open AccessArticle

Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms

1
Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 16, 44227 Dortmund, Germany
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Computer Engineering Department, An-Najah National University, Nablus P.O. Box 7, Palestine
3
Biomedical Research Department, Leibniz Institute for Analytical Sciences, ISAS e.V., Bunsen-Kirchhoff-Straße 11, 44139 Dortmund, Germany
*
Author to whom correspondence should be addressed.
This paper is an extended version of a conference paper published in Yayla, M.; Toma, A.; Lenssen, J.E.; Shpacovitch, V.; Chen, K.-H.; Weichert, F.; Chen, J.-J. Resource-Efficient Nanoparticle Classification Using Frequency Domain Analysis. In Proceedings of Bildverarbeitung für die Medizin (BVM), Lübeck, Germany, 15–17 March, 2019.
Sensors 2019, 19(19), 4138; https://doi.org/10.3390/s19194138
Received: 28 August 2019 / Revised: 19 September 2019 / Accepted: 23 September 2019 / Published: 24 September 2019
A mobile system that can detect viruses in real time is urgently needed, due to the combination of virus emergence and evolution with increasing global travel and transport. A biosensor called PAMONO (for Plasmon Assisted Microscopy of Nano-sized Objects) represents a viable technology for mobile real-time detection of viruses and virus-like particles. It could be used for fast and reliable diagnoses in hospitals, airports, the open air, or other settings. For analysis of the images provided by the sensor, state-of-the-art methods based on convolutional neural networks (CNNs) can achieve high accuracy. However, such computationally intensive methods may not be suitable on most mobile systems. In this work, we propose nanoparticle classification approaches based on frequency domain analysis, which are less resource-intensive. We observe that on average the classification takes 29 μ s per image for the Fourier features and 17 μ s for the Haar wavelet features. Although the CNN-based method scores 1–2.5 percentage points higher in classification accuracy, it takes 3370 μ s per image on the same platform. With these results, we identify and explore the trade-off between resource efficiency and classification performance for nanoparticle classification of images provided by the PAMONO sensor. View Full-Text
Keywords: nanoparticles; frequency domain analysis; mobile sensors; PAMONO biosensor; surface plasmon resonance; embedded systems nanoparticles; frequency domain analysis; mobile sensors; PAMONO biosensor; surface plasmon resonance; embedded systems
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Yayla, M.; Toma, A.; Chen, K.-H.; Lenssen, J.E.; Shpacovitch, V.; Hergenröder, R.; Weichert, F.; Chen, J.-J. Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms. Sensors 2019, 19, 4138.

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