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9 April 2020 Embedded neural network system for microorganisms growth analysis
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Proceedings Volume 11457, Saratov Fall Meeting 2019: Optical and Nano-Technologies for Biology and Medicine; 1145720 (2020)
Event: Saratov Fall Meeting 2019: VII International Symposium on Optics and Biophotonics, 2019, Saratov, Russian Federation
This study presents autonomous system for microorganisms’ growth analysis in laboratory environment. As shown in previous research, laser speckle analysis allows detecting submicron changes of substrate with growing bacteria. By using neural networks for speckle analysis, it is possible to develop autonomous system, that can evaluate microorganisms’ growth by using cheap optics and electronics elements.

System includes embedded processing module, CMOS camera, 670nm laser diode and optionally WiFi module for connecting to external image storage system. Due to small size, system could be fully placed in laboratory incubator with constant humidity and temperature. By using laser diode, Petri dish with microorganisms’ substrate is illuminated with speckle pattern. Embedded camera and processing system obtain images and stores them for processing with neural network.

Neural network utilizes “3D ConvNets” architecture with ability to encode not only spatial speckle variance, but also their changes in time. Convolutive approach allows significantly reduce the number of trained parameters, therefore reducing training and detection time. Neural network training used 200 bacteria colonies and additional 300 areas without bacteria. In the result, trained neural network reaches 0.95 accuracy score, that proves correctness of the approach.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
D. Bliznuks, Y. Chizhov, A. Bondarenko, D. Uteshev, J. Liepins, S. Zolins, A. Lihachev, and I. Lihacova "Embedded neural network system for microorganisms growth analysis", Proc. SPIE 11457, Saratov Fall Meeting 2019: Optical and Nano-Technologies for Biology and Medicine, 1145720 (9 April 2020);


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