Abstract
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 s per image for the Fourier features and 17 s for the Haar wavelet features. Although the CNN-based method scores 1–2.5 percentage points higher in classification accuracy, it takes 3370 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.
Highlights
Due to the evolution and emergence of viruses, together with increasing global travel and transport, there is the risk of spreading epidemic diseases
As examples of frequency domain analysis methods, we evaluate the use of Fourier and Haar wavelet features, i.e., fast Fourier transform (FFT) and fast wavelet transform (FWT) features
To evaluate the execution time of the frequency domain analysis on an embedded platform, we executed the classification as a CPU-only version on the ARM Cortex-A9 as well
Summary
Due to the evolution and emergence of viruses, together with increasing global travel and transport, there is the risk of spreading epidemic diseases. An accessible mobile real-time virus detection device is needed. One portable sensor for the detection of viruses in liquid samples is the Plasmon Assisted Microscopy of Nano-size Objects (PAMONO) biosensor [1,2]. It addresses many application scenarios, since it enables fast and quick diagnoses in settings such as hospitals, airports, and in the open air. The sensor provides a sequence of images, which are analyzed by an image analysis pipeline to detect the presence of viruses and virus-like particles (VLPs).
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