Development of Blood Glucose Monitoring System using Image Processing and Machine Learning Techniques
Abstract
Glucose concentration measurement is essential for
diagnosis, monitoring and treatment of various medical conditions
like diabetes mellitus, hypoglycemia, etc. This paper presents a
novel image-processing and machine learning based approach for
glucose concentration measurement. Experimentation based on
Glucose oxidase - peroxidase (GOD/POD) method has been
performed to create the database. Glucose in the sample reacts
with the reagent wherein the concentration of glucose is detected
using colorimetric principle. Colour intensity thus produced, is
proportional to the glucose concentration and varies at different
levels. Existing clinical chemistry analyzers use spectrophotometry
to estimate the glucose level of the sample. Instead, this developed
system uses simplified hardware arrangement and estimates
glucose concentration by capturing the image of the sample. After
further processing, its Saturation (S) and Luminance (Y) values
are extracted from the captured image. Linear regression based
machine learning algorithm is used for training the dataset consists
of saturation and luminance values of images at different
concentration levels. Integration of machine learning provides the
benefit of improved accuracy and predictability in determining
glucose level. The detection of glucose concentrations in the range
of 10–400 mg/dl has been evaluated. The results of the developed
system were verified with the currently used spectrophotometry
based Trace40 clinical chemistry analyzer. The deviation of the
estimated values from the actual values was found to be around 2-
3%.
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