Development of Blood Glucose Monitoring System using Image Processing and Machine Learning Techniques
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 base...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Polish Academy of Sciences
2022-06-01
|
| Series: | International Journal of Electronics and Telecommunications |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/123402/PDF/45-50-3450-Thomas-sl-b-new.pdf |
| _version_ | 1811314571993939968 |
|---|---|
| author | Angel Thomas Sangeeta Palekar Jayu Kalambe |
| author_facet | Angel Thomas Sangeeta Palekar Jayu Kalambe |
| author_sort | Angel Thomas |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-04-13T11:14:57Z |
| format | Article |
| id | doaj.art-eab503e1fd9f434780870fe3ddbcb1d4 |
| institution | Directory Open Access Journal |
| issn | 2081-8491 2300-1933 |
| language | English |
| last_indexed | 2024-04-13T11:14:57Z |
| publishDate | 2022-06-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | International Journal of Electronics and Telecommunications |
| spelling | doaj.art-eab503e1fd9f434780870fe3ddbcb1d42022-12-22T02:49:01ZengPolish Academy of SciencesInternational Journal of Electronics and Telecommunications2081-84912300-19332022-06-01vol. 68No 2323328https://doi.org/10.24425/ijet.2022.139885Development of Blood Glucose Monitoring System using Image Processing and Machine Learning TechniquesAngel Thomas0Sangeeta Palekar1Jayu Kalambe2Shri Ramdeobaba College of Engineering & Management, IndiaShri Ramdeobaba College of Engineering & Management, IndiaShri Ramdeobaba College of Engineering & Management, IndiaGlucose 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%.https://journals.pan.pl/Content/123402/PDF/45-50-3450-Thomas-sl-b-new.pdfglucoseimage processingmachine learningcolorimetry |
| spellingShingle | Angel Thomas Sangeeta Palekar Jayu Kalambe Development of Blood Glucose Monitoring System using Image Processing and Machine Learning Techniques International Journal of Electronics and Telecommunications glucose image processing machine learning colorimetry |
| title | Development of Blood Glucose Monitoring System using Image Processing and Machine Learning Techniques |
| title_full | Development of Blood Glucose Monitoring System using Image Processing and Machine Learning Techniques |
| title_fullStr | Development of Blood Glucose Monitoring System using Image Processing and Machine Learning Techniques |
| title_full_unstemmed | Development of Blood Glucose Monitoring System using Image Processing and Machine Learning Techniques |
| title_short | Development of Blood Glucose Monitoring System using Image Processing and Machine Learning Techniques |
| title_sort | development of blood glucose monitoring system using image processing and machine learning techniques |
| topic | glucose image processing machine learning colorimetry |
| url | https://journals.pan.pl/Content/123402/PDF/45-50-3450-Thomas-sl-b-new.pdf |
| work_keys_str_mv | AT angelthomas developmentofbloodglucosemonitoringsystemusingimageprocessingandmachinelearningtechniques AT sangeetapalekar developmentofbloodglucosemonitoringsystemusingimageprocessingandmachinelearningtechniques AT jayukalambe developmentofbloodglucosemonitoringsystemusingimageprocessingandmachinelearningtechniques |