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6 March 2023 Rapid detection and classification of bacterial colonies using a thin film transistor (TFT) image sensor and deep learning
Yuzhu Li, Tairan Liu, Hatice C. Koydemir, Hongda Wang, Keelan O'Riordan, Bijie Bai, Yuta Haga, Junji Kobashi, Hitoshi Tanaka, Takaya Tamaru, Kazunori Yamaguchi, Aydogan Ozcan
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Proceedings Volume PC12369, Optics and Biophotonics in Low-Resource Settings IX; PC123690A (2023) https://doi.org/10.1117/12.2648167
Event: SPIE BiOS, 2023, San Francisco, California, United States
Abstract
We present a high-throughput and automated system for the early detection and classification of bacterial colony-forming units (CFUs) using a thin-film transistor (TFT) image sensor. A lens-free imager was built using the TFT sensor with a ~7 cm2 field-of-view to collect the time-lapse images of bacterial colonies. Two trained neural networks were used to detect and classify the bacterial colonies based on their spatio-temporal features. Our system achieved an average CFU detection rate of 97.3% at 9 hours of incubation and an average CFU recovery rate of 91.6% at ~12 hours, saving ~12 hours compared to the EPA-approved method.
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Yuzhu Li, Tairan Liu, Hatice C. Koydemir, Hongda Wang, Keelan O'Riordan, Bijie Bai, Yuta Haga, Junji Kobashi, Hitoshi Tanaka, Takaya Tamaru, Kazunori Yamaguchi, and Aydogan Ozcan "Rapid detection and classification of bacterial colonies using a thin film transistor (TFT) image sensor and deep learning", Proc. SPIE PC12369, Optics and Biophotonics in Low-Resource Settings IX, PC123690A (6 March 2023); https://doi.org/10.1117/12.2648167
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KEYWORDS
Image sensors
Image classification
Thin films
Transistors
Bacteria
Classification systems
Environmental sensing
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