대한의용생체공학회:의공학회지 (Journal of Biomedical Engineering Research)

  • 제42권5호
  • /
  • Pages.241-249
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  • 2021
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  • 1229-0807(pISSN)
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  • 2288-9396(eISSN)
대한의용생체공학회 (The Korean Society of Medical and Biological Engineering)
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자궁경부 영상에서의 라디오믹스 기반 판독 불가 영상 분류 알고리즘 연구

A Radiomics-based Unread Cervical Imaging Classification Algorithm

  • 김고은 (가천대학교 의용생체공학과) ;
  • 김영재 (가천대학교 의용생체공학과) ;
  • 주웅 (이화여자대학교 산부인과) ;
  • 남계현 (순천향대학교 산부인과) ;
  • 김수녕 (엔티엘의료재단 R&D센터) ;
  • 김광기 (가천대학교 의용생체공학과)
  • Kim, Go Eun (Department of Biomedical Engineering, Gachon University) ;
  • Kim, Young Jae (Department of Biomedical Engineering, Gachon University) ;
  • Ju, Woong (Department of Obstetrics & Gynecology, Ewha Womans University Seoul Hospital) ;
  • Nam, Kyehyun (Department of Obstetrics & Gynecology, Soonchunhyang University, Bucheon Hospital) ;
  • Kim, Soonyung (R&D Center, NTL Medical Institute) ;
  • Kim, Kwang Gi (Department of Biomedical Engineering, Gachon University)
  • 투고 : 2021.10.27
  • 심사 : 2021.10.29
  • 발행 : 2021.10.31
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초록

Recently, artificial intelligence for diagnosis system of obstetric diseases have been actively studied. Artificial intelligence diagnostic assist systems, which support medical diagnosis benefits of efficiency and accuracy, may experience problems of poor learning accuracy and reliability when inappropriate images are the model's input data. For this reason, before learning, We proposed an algorithm to exclude unread cervical imaging. 2,000 images of read cervical imaging and 257 images of unread cervical imaging were used for this study. Experiments were conducted based on the statistical method Radiomics to extract feature values of the entire images for classification of unread images from the entire images and to obtain a range of read threshold values. The degree to which brightness, blur, and cervical regions were photographed adequately in the image was determined as classification indicators. We compared the classification performance by learning read cervical imaging classified by the algorithm proposed in this paper and unread cervical imaging for deep learning classification model. We evaluate the classification accuracy for unread Cervical imaging of the algorithm by comparing the performance. Images for the algorithm showed higher accuracy of 91.6% on average. It is expected that the algorithm proposed in this paper will improve reliability by effectively excluding unread cervical imaging and ultimately reducing errors in artificial intelligence diagnosis.

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과제정보

이 연구는 중소벤처기업부에서 지원하는 기술 개발프로그램(S2797147)과 가천대 길병원(FRD2019-11-02(3))의 지원을 받아 수행되었습니다.

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