Rapid, portable and cost-effective yeast cell viability and concentration analysis using lensfree on-chip microscopy and machine learning

Abstract

We demonstrate a field-portable and automatic yeast analysis platform that can rapidly measure cell concentration and viability using on-chip microscopy and machine learning.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 01, 2016
Source ID
10.1039/c6lc00976j

Entities

People

  • Alborz Feizi
  • Alex Guziak
  • Alon Greenbaum
  • Aydoğan Özcan
  • Brandon Berg
  • Chung‐Tse Michael Wu
  • Haydar Ozkan
  • Michelle Luong
  • Raymond Yan Lok Chan
  • Wei Luo
  • Yibo Zhang
  • Yichen Wu

Organizations

  • Army Research Office
  • California Institute of Technology
  • Howard Hughes Medical Institute
  • National Institutes of Health
  • National Science Foundation
  • Office of Naval Research
  • United States Army
  • University of California
  • University of California, Los Angeles
  • University of Michigan

Tags

Fields of Study

  • Computer science

Readers

  • Military/Explosive Ordnance Disposal (EOD) Technology
  • Molecular Genetics
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks