Lensfree on-chip microscopy achieves accurate measurement of yeast cell viability and concentration using machine learning

Feizi, AlborzAlborzFeiziZhang, YiboYiboZhangGreenbaum, AlonAlonGreenbaumGuziak, AlexAlexGuziakLuong, MichelleMichelleLuongYan Lok Chan, RaymondRaymondYan Lok ChanBerg, BrandonBrandonBergOzkan, HaydarHaydarOzkanLuo, WeiWeiLuoCHUNG-TSE WUWu, YichenYichenWuOzcan, AydoganAydoganOzcan2024-03-062024-03-062017-01-019781943580279https://scholars.lib.ntu.edu.tw/handle/123456789/640373Automatic measurement of yeast viability and concentration is achieved by coupling a lensfree on-chip holographic microscope with a machine learning based classification algorithm that counts the number of live/dead cells stained with methylene blue.Lensfree on-chip microscopy achieves accurate measurement of yeast cell viability and concentration using machine learningconference paper10.1364/CLEO_AT.2017.ATh4B.42-s2.0-85020483990https://api.elsevier.com/content/abstract/scopus_id/85020483990