[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122955-en":3,"doc-seo-122955-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122955,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",7,"Healthcare","Development of Blood Glucose Monitoring System using Image Processing and Machine Learning Techniques","Glucose concentration measurement underpins accurate diagnosis, monitoring, and treatment of conditions such as diabetes mellitus and hypoglycemia. The work proposes an image-processing and machine-learning approach that estimates glucose levels from captured sample images. A GOD/POD colorimetric reaction generates color intensity proportional to glucose concentration, while extracted image Saturation (S) and Luminance (Y) values feed a linear regression model trained on multiple concentration levels. The method evaluates 10–400 mg/dl and is verified against a Trace40 spectrophotometry analyzer, showing an estimated deviation of about 2–3%.","INTL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS, 2022, VOL. 68, NO. 2, PP. 323-328  \nManuscript received August 13, 2021; revised May, 2022. DOI: 10.24425/ijet.2022.139885  \nDevelopment of Blood Glucose Monitoring System using Image Processing and Machine  \nLearning Techniques  \nAngel Thomas, Sangeeta Palekar, and Jayu Kalambe  \nAbstract—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%.  \nKeywords—glucose; image processing; machine learning; colorimetry  \nI. INTRODUCTION  \nONisEgloufctohsee  \nmost common and essential biochemical assay concentration measurement. It is necessary for  \ndiagnosis and monitoring of diabetes mellitus, a major health problem around the world. This disease is becoming common day by day and a number of lifestyle factors such as urbanization, diet, stress, physical activity or lack of exercise are known to have contribution in the development of diabetes. Diabetes is a chronic disease which is caused due to completely or partially insufficient insulin production in the body or ineffective utilization of insulin by the body [1], [2] . Insulin is the blood sugar regulating hormone. In 2014, WHO reported an increase of 314 million diabetes patients in a period of 30 years [3] . In 2019, 1.5 million deaths were claimed to have been directly caused due to diabetes. When prolonged and left uncontrolled, diabetes can cause serious health conditions like cardiovascular diseases, neuropathy, nephropathy, and  \nAuthors are with Shri Ramdeobaba College of Engineering & Management, India (e-mail: [thomasas@rknec.edu](thomasas@rknec.edu), [palekarsd1@rknec.edu](palekarsd1@rknec.edu), kalambej@ [rknec.edu](rknec.edu)).  \nretinopathy [4]–[7] . It can be treated and its consequences can be avoided or delayed by proper monitoring of blood glucose. Inaccurate measurement of blood glucose can lead to improper diagnosis or treatment. It may even lead to hypoglycemia due to over dosage of insulin. Therefore, quality of glucose monitoring should be high in terms of the accuracy of the results. Accurate monitoring using quality glucose monitoring systems certainly helps regulate diabetes and avoid its ill-effects [8], [9] .  \nSeveral devices employing various techniques for glucose assay are available. Some of these techniques arespectrophotometric [10], electrochemical [11], [12], polarometric [13], amperometric [14], and so on. One of the methods for analyte detection is colorimetry which is popularly use","cbCaiuFG2InTGuhw","https://ap.wps.com/l/cbCaiuFG2InTGuhw","pdf",709699,1,6,"English","en",105,"# Abstract\n# Introduction\n## Diabetes need and monitoring importance\n## Glucose assay techniques and colorimetry background\n## Spectrophotometric analyzers and Beer-Lambert law","[{\"question\":\"What principle does the proposed system use to relate image features to glucose concentration?\",\"answer\":\"The system relies on the GOD/POD colorimetric reaction, where color intensity changes with glucose concentration. It then extracts image Saturation (S) and Luminance (Y) values from the captured sample to estimate glucose level.\"},{\"question\":\"How is the machine learning model trained in this approach?\",\"answer\":\"A dataset is created from images at different concentration levels, and the extracted S and Y values are used for training. A linear regression-based algorithm learns the relationship between the image features and glucose concentration.\"},{\"question\":\"How does the system’s accuracy compare with a conventional spectrophotometry analyzer?\",\"answer\":\"Results are verified against a Trace40 clinical chemistry analyzer using spectrophotometry. The deviation between estimated and actual values is reported to be around 2–3%.\"}]","Development of Blood Glucose Monitoring System using Image Processing and Machine Learning Techniques | PDF",1785813858,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"development-of-blood-glucose-monitoring-system-using-image-processing-and-machine-learning-techniques","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/development-of-blood-glucose-monitoring-system-using-image-processing-and-machine-learning-techniques/122955/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What principle does the proposed system use to relate image features to glucose concentration?","Question",{"text":75,"@type":76},"The system relies on the GOD/POD colorimetric reaction, where color intensity changes with glucose concentration. It then extracts image Saturation (S) and Luminance (Y) values from the captured sample to estimate glucose level.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the machine learning model trained in this approach?",{"text":80,"@type":76},"A dataset is created from images at different concentration levels, and the extracted S and Y values are used for training. A linear regression-based algorithm learns the relationship between the image features and glucose concentration.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the system’s accuracy compare with a conventional spectrophotometry analyzer?",{"text":84,"@type":76},"Results are verified against a Trace40 clinical chemistry analyzer using spectrophotometry. 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