Location
https://www.kennesaw.edu/ccse/events/computing-showcase/fa24-cday-program.php
Event Website
https://iotas.kennesaw.edu/
Document Type
Event
Start Date
19-11-2024 4:00 PM
Description
This study introduces a novel computer vision-based spectral approach for non-invasive glucose detection using synthetic blood samples. We developed an experimental setup with glucose concentrations from 70 to 120 mg/dL, using two dye methods. Light sources tested included an 850 nm LED, 850 nm laser, 808 nm laser, and 650 nm laser, with image capture via a 1080p IR camera. Data augmentation, including Gaussian noise, contrast and brightness adjustments, rotations, and zooming, produced seven variants per image. Three machine learning models—CNN, AdaBoost, and ResNet—were evaluated, with the 850 nm light source yielding the best results: 87.5% of predictions fell within Zone A of the Clarke Error Grid. Findings support the potential of this approach for non-invasive glucose monitoring.
Included in
GPR-1194 Computer Vision-Enhanced Spectroscopy for Glucose Prediction: An In Vitro Validation Study
https://www.kennesaw.edu/ccse/events/computing-showcase/fa24-cday-program.php
This study introduces a novel computer vision-based spectral approach for non-invasive glucose detection using synthetic blood samples. We developed an experimental setup with glucose concentrations from 70 to 120 mg/dL, using two dye methods. Light sources tested included an 850 nm LED, 850 nm laser, 808 nm laser, and 650 nm laser, with image capture via a 1080p IR camera. Data augmentation, including Gaussian noise, contrast and brightness adjustments, rotations, and zooming, produced seven variants per image. Three machine learning models—CNN, AdaBoost, and ResNet—were evaluated, with the 850 nm light source yielding the best results: 87.5% of predictions fell within Zone A of the Clarke Error Grid. Findings support the potential of this approach for non-invasive glucose monitoring.
https://digitalcommons.kennesaw.edu/cday/Fall_2024/PhD_Research/1