Poster Title
P-24 Mobile Application for Biosensor Colorimetric Analysis
Abstract
Inexpensive paper-based biosensors can be valuable screening tools to test for various illnesses, but it is often challenging to design them to produce a visual change that can easily be identified by untrained users. This research aims to compensate for the lack of distinct visual cues by developing a mobile application that will use machine learning to analyze a picture of a sensor and determine whether it shows a positive or negative result. The machine learning algorithm will be trained on a set of labeled sensor images and its classification accuracy will be calculated and compared to a human expert.
Thesis Record URL
https://digitalcommons.andrews.edu/honors/145
Location
Buller Hall
Start Date
2-26-2016 2:30 PM
End Date
2-26-2016 4:00 PM
P-24 Mobile Application for Biosensor Colorimetric Analysis
Buller Hall
Inexpensive paper-based biosensors can be valuable screening tools to test for various illnesses, but it is often challenging to design them to produce a visual change that can easily be identified by untrained users. This research aims to compensate for the lack of distinct visual cues by developing a mobile application that will use machine learning to analyze a picture of a sensor and determine whether it shows a positive or negative result. The machine learning algorithm will be trained on a set of labeled sensor images and its classification accuracy will be calculated and compared to a human expert.
Acknowledgments
Dr. Rodney Summerscales