TY - GEN
T1 - Using Machine Learning to Objectively Determine Colorimetric Assay Results from Cell Phone Photos Taken under Ambient Lighting
AU - Fisher, Rachel
AU - Anderson, Karen
AU - Christen, Jennifer Blain
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/9
Y1 - 2021/8/9
N2 - Colorimetric assays are an important tool in point-of-care testing that offers several advantages such as rapid response times and inexpensive costs. A factor that currently limits their use is objective measures to determine results. Current solutions consist of creating a test reader that standardizes the conditions the strip is under before measuring. However, this increases the cost and decreases the portability of these assays. The focus of this study is to train a convolutional neural network (CNN) that can objectively determine results of colorimetric assays under varying conditions. To ensure the flexibility of the model to several types of colorimetric assays, three models are trained on the same CNN. The images these models are trained on consist of positive and negative images of ETG (99.87% positive classification, 99.96% negative classification), fentanyl (99.60% positive classification, 99.56% negative classification), and HPV antibody (99.86% positive classification, 100% negative classification) strips taken under different lighting and background conditions. A fourth model is trained on an image set composed of all three strip types with the lowest classification accuracy being 99.11%.
AB - Colorimetric assays are an important tool in point-of-care testing that offers several advantages such as rapid response times and inexpensive costs. A factor that currently limits their use is objective measures to determine results. Current solutions consist of creating a test reader that standardizes the conditions the strip is under before measuring. However, this increases the cost and decreases the portability of these assays. The focus of this study is to train a convolutional neural network (CNN) that can objectively determine results of colorimetric assays under varying conditions. To ensure the flexibility of the model to several types of colorimetric assays, three models are trained on the same CNN. The images these models are trained on consist of positive and negative images of ETG (99.87% positive classification, 99.96% negative classification), fentanyl (99.60% positive classification, 99.56% negative classification), and HPV antibody (99.86% positive classification, 100% negative classification) strips taken under different lighting and background conditions. A fourth model is trained on an image set composed of all three strip types with the lowest classification accuracy being 99.11%.
KW - CNN
KW - colorimetric assays
KW - machine learning
KW - non-standard conditions
UR - http://www.scopus.com/inward/record.url?scp=85115604764&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85115604764&partnerID=8YFLogxK
U2 - 10.1109/MWSCAS47672.2021.9531902
DO - 10.1109/MWSCAS47672.2021.9531902
M3 - Conference contribution
AN - SCOPUS:85115604764
T3 - Midwest Symposium on Circuits and Systems
SP - 467
EP - 470
BT - 2021 IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2021
Y2 - 9 August 2021 through 11 August 2021
ER -