FURI | Fall 2020
Machine Learning Model for Classifying Colorimetric Assays
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.
Hometown: Gilbert, Arizona, United States
Graduation date: Spring 2021