Date of Award
3-28-2016
Document Type
Honors Thesis
Department
Engineering & Computer Science
First Advisor
Rodney L. Summerscales
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 examines one method of compensating for the lack of distinct visual cues by developing and testing a mobile application that uses a machine learning algorithm (k-Nearest Neighbors) to analyze a picture of a sensor and determine whether it shows a positive or negative result. The machine learning algorithm was trained on a set of labeled sensor images and k-fold cross-validation was used to analyze its classification accuracy.
Recommended Citation
You, Eui Bin, "Mobile Application for Biosensor Colorimetric Analysis" (2016). Honors Theses. 145.
https://dx.doi.org/10.32597/honors/145/
https://digitalcommons.andrews.edu/honors/145
Creative Commons License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 International License.
DOI
https://dx.doi.org/10.32597/honors/145/