Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms †

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 [Formula: see text] s per image for the Fourier features and 17 [Formula: see text] s for the Haar wavelet features. Although the CNN-based method scores 1–2.5 percentage points higher in classification accuracy, it takes 3370 [Formula: see text] 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.