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http://dx.doi.org/10.3745/KTCCS.2021.10.11.305

Urinary Stones Segmentation Model and AI Web Application Development in Abdominal CT Images Through Machine Learning  

Lee, Chung-Sub (원광대학교 의료융합연구센터)
Lim, Dong-Wook (원광대학교 의료융합연구센터)
Noh, Si-Hyeong (원광대학교 의료융합연구센터)
Kim, Tae-Hoon (원광대학교병원 스마트사업팀)
Park, Sung-Bin (중앙대학교병원 의학과)
Yoon, Kwon-Ha (중앙대학교병원)
Jeong, Chang-Won (원광대학교병원 스마트사업팀)
Publication Information
KIPS Transactions on Computer and Communication Systems / v.10, no.11, 2021 , pp. 305-310 More about this Journal
Abstract
Artificial intelligence technology in the medical field initially focused on analysis and algorithm development, but it is gradually changing to web application development for service as a product. This paper describes a Urinary Stone segmentation model in abdominal CT images and an artificial intelligence web application based on it. To implement this, a model was developed using U-Net, a fully-convolutional network-based model of the end-to-end method proposed for the purpose of image segmentation in the medical imaging field. And for web service development, it was developed based on AWS cloud using a Python-based micro web framework called Flask. Finally, the result predicted by the urolithiasis segmentation model by model serving is shown as the result of performing the AI web application service. We expect that our proposed AI web application service will be utilized for screening test.
Keywords
Urinary Stone; DICOM; Artificial Intelligence; Model Serving; Flask;
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