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9 March 2020 Deep learning enables high-throughput early detection and classification of bacterial colonies using time-lapse coherent imaging (Conference Presentation)
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Abstract
We report a highly-sensitive, high-throughput, and cost-effective bacteria identification system which continuously captures and reconstructs holographic images of an agar-plate and analyzes the time-lapsed images with deep learning models for early detection of colonies. The performance of our system was confirmed by detection and classification of Escherichia coli, Enterobacter aerogenes, and Klebsiella pneumoniae in water samples. We detected 90% of the bacterial colonies and their growth within 7-10h (>95% within 12h) with ~100% precision, and correctly identified the corresponding species within 7.6-12h with 80% accuracy, and achieved time savings of >12h as compared to the gold-standard EPA-approved methods.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hongda Wang, Hatice C. Koydemir, Yunzhe Qiu, Bijie Bai, Yibo Zhang, Yiyin Jin, Sabiha Tok, Enis C. Yilmaz, Esin Gumustekin, Yilin Luo, Yair Rivenson, and Aydogan Ozcan "Deep learning enables high-throughput early detection and classification of bacterial colonies using time-lapse coherent imaging (Conference Presentation)", Proc. SPIE 11230, Optics and Biophotonics in Low-Resource Settings VI, 112300E (9 March 2020); https://doi.org/10.1117/12.2547399
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