{"title":"An Alternate Solution for Smartphone-Based Urinalysis","authors":"Abir Ebna Harun, Mohammad Ashfak Habib","doi":"10.1109/ICEEE54059.2021.9718777","DOIUrl":null,"url":null,"abstract":"Urinalysis is a common medical test that can be costly and inconvenient in medical facilities. The use of point-of-care(POC) test devices, smartphones, manifolds, and other additional tools can make urinalysis easier in a home-based environment. In this paper, we are proposing a new system that can be used to performing a laboratory-free urinalysis with the help of a urine test strip and a smartphone device. Our system contains several image pre-processing steps and an artificial neural network mapping model to analyze the color pixels of the urine test strip. By following our proposed solution, the user can acquire an accurate computer vision integrated urinalysis result.","PeriodicalId":188366,"journal":{"name":"2021 3rd International Conference on Electrical & Electronic Engineering (ICEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 3rd International Conference on Electrical & Electronic Engineering (ICEEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEEE54059.2021.9718777","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
Urinalysis is a common medical test that can be costly and inconvenient in medical facilities. The use of point-of-care(POC) test devices, smartphones, manifolds, and other additional tools can make urinalysis easier in a home-based environment. In this paper, we are proposing a new system that can be used to performing a laboratory-free urinalysis with the help of a urine test strip and a smartphone device. Our system contains several image pre-processing steps and an artificial neural network mapping model to analyze the color pixels of the urine test strip. By following our proposed solution, the user can acquire an accurate computer vision integrated urinalysis result.
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