FURI | Fall 2020

Machine Learning Model for Classifying Colorimetric Assays

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The focus of this study is to design a machine learning model that can classify alcohol test strips as positive or negative from cell phone photos taken under non-standard conditions. A software algorithm that can objectively determine results from colorimetric assays under non-standard conditions will improve the accessibility and portability of these strips, supporting point-of-care testing. To do this, the team is currently training a model from images of test strips. The accuracy of classifying the samples under these conditions will be evaluated and is expected to be able to adequately provide qualitative results.

Student researcher

Rachel Fisher

Rachel Fisher

Biomedical engineering

Hometown: Gilbert, Arizona, United States

Graduation date: Spring 2021