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9 September 2019 Detecting and discriminating between different types of bacteria with a low-cost smartphone based optical device and neural network models
Venkat Balagurusamy, Vince Siu, Ashwin Dhinesh Kumar, Sahil Dureja, Joseph Ligman, Prabhakar Kudva, Matt Tong, Donna Dillenberger
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Proceedings Volume 11087, Biosensing and Nanomedicine XII; 110870E (2019) https://doi.org/10.1117/12.2529829
Event: SPIE Nanoscience + Engineering, 2019, San Diego, California, United States
Abstract
The food industry such as meat producers and plant product processors have tremendous interest in detecting pathogenic organisms such as E.coli, Listeria, and Salmonella in very low concentrations down to a single cell. These pathogenic organisms when they are in the right environment can start multiplying exponentially. For example, E.coli cells can double every 20 minutes posing a tremendous danger for their growth in over many hours. We have designed an optical device that attaches to a smartphone providing an imaging and processing device that achieves an optical resolution of 1 micron. The optics is engineered to reduce aberrations in the system. We also developed a smartphone application that can track microbeads and bacteria in the video frames in real time using computer vision algorithms. We extract individual bacterial image segments in these videos to train neural network models to detect and differentiate different types of bacteria such as E.coli and B.subtilis. These trained models can detect and discriminate E.coli from B.subtilis with high accuracy of more than 80%. This approach has the potential to train different types of bacteria with a multiclass neural network classifier by training them with images from different genera and species of bacteria. Such a classifier can detect them in a wild sample containing many types of bacteria with low-cost smartphone optical device.
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Venkat Balagurusamy, Vince Siu, Ashwin Dhinesh Kumar, Sahil Dureja, Joseph Ligman, Prabhakar Kudva, Matt Tong, and Donna Dillenberger "Detecting and discriminating between different types of bacteria with a low-cost smartphone based optical device and neural network models", Proc. SPIE 11087, Biosensing and Nanomedicine XII, 110870E (9 September 2019); https://doi.org/10.1117/12.2529829
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Cited by 2 scholarly publications.
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KEYWORDS
Bacteria
Image processing
Neural networks
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