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http://dx.doi.org/10.5407/jksv.2021.19.2.041

Development of microfluidic green algae cell counter based on deep learning  

Cho, Seongsu (School of Mechanical Engineering, Sungkyunkwan University)
Shin, Seonghun (School of Mechanical Engineering, Sungkyunkwan University)
Sim, Jaemin (School of Mechanical Engineering, Sungkyunkwan University)
Lee, Jinkee (School of Mechanical Engineering, Sungkyunkwan University)
Publication Information
Journal of the Korean Society of Visualization / v.19, no.2, 2021 , pp. 41-47 More about this Journal
Abstract
River and stream are the important water supply source in our lives. Eutrophication causes excessive green algae growth including microcystis, which makes harmful to ecosystem and human health. Therefore, the water purification process to remove green algae is essential. In Korea, green algae alarm system exists depending on the concentration of green algae cells in river or stream. To maintain the growth amount under control, green algae monitoring system is being used. However, the unmanned, small and automatic monitoring system would be preferable. In this study, we developed the 3D printed device to measure the concentration of green algae cell using microfluidic droplet generator and deep learning. Deep learning network was trained by using transfer learning through pre-trained deep learning network. This newly developed microfluidic cell counter has sufficient accuracy to be possibly applicable to green algae alarm system.
Keywords
Green algae; Cell counting; Microfluidic device; Deep learning; Droplet generator;
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