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Classification of chemically modified red blood cells in microflow using machine learning video analysis

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 02 Jun 2023Publisher:Zenodo
Authors: Raj Kumar Rajaram Baskaran; Bernd Porr; Andreas Link; Thomas Franke;
Code repository: https://github.com/berndporr/MicrofluidicsZigZagVideoAI/tree/1.0

doi: 10.5281/zenodo.7997895 , 10.5281/zenodo.7997896 , 10.5281/zenodo.8126539

Classification of chemically modified red blood cells in microflow using machine learning video analysis

Abstract

This is a revised version of the code which now makes it possible to follow back from the results to the original video files for example to see if they are in focus or out of focus. The statistical precision has also been increased by using now 100 videos for validation and testing each.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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