Classification of urine components using supervised machine learning based on physical particle data retrieved by digital holographic microscopy

Yussef Hanna; Marlene Kallaß; Álvaro Barroso; Jürgen Schnekenburger; Klaus Brinker; Björn Kemper

doi:10.1364/DH.2022.Th2A.5

Digital Holography and 3-D Imaging 2022
Technical Digest Series (Optica Publishing Group, 2022),
paper Th2A.5
•https://doi.org/10.1364/DH.2022.Th2A.5

Classification of urine components using supervised machine learning based on physical particle data retrieved by digital holographic microscopy

Yussef Hanna, Marlene Kallaß, Álvaro Barroso, Jürgen Schnekenburger, Klaus Brinker, and Björn Kemper

Digital Holography and Three-Dimensional Imaging 2022

Cambridge United Kingdom
1–4 August 2022
ISBN: 978-1-957171-12-8

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Joint Poster Session III (Th2A)

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Y. Hanna, M. Kallaß, Á. Barroso, J. Schnekenburger, K. Brinker, and B. Kemper, "Classification of urine components using supervised machine learning based on physical particle data retrieved by digital holographic microscopy," in Digital Holography and 3-D Imaging 2022, D. Chu, J. Park, C. Cheng, and P. Ferraro, eds., Technical Digest Series (Optica Publishing Group, 2022), paper Th2A.5.

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Abstract

We explored the capabilities of supervised machine learning to classify urine sediment based on physical parameters retrieved from quantitative digital holographic phase contrast images.

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