Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Research on the Lesion Classification by Radiomics in Laryngoscopy Image

후두내시경 영상에서의 라디오믹스에 의한 병변 분류 연구

  • Park, Jun Ha (Department of Biomedical Eng., College of Health & Sci., Gachon University) ;
  • Kim, Young Jae (Department of Biomedical Eng., College of Health & Sci., Gachon University) ;
  • Woo, Joo Hyun (Department of Otorhinolaryngology-Head and Neck Surgery, Gachon University School of Medicine, Gil Medical Center) ;
  • Kim, Kwang Gi (Department of Biomedical Eng., College of Health & Sci., Gachon University)
  • 박준하 (가천대학교 보건과학대학 의용생체공학과) ;
  • 김영재 (가천대학교 보건과학대학 의용생체공학과) ;
  • 우주현 (가천대길병원 이비인후과) ;
  • 김광기 (가천대학교 보건과학대학 의용생체공학과)
  • Received : 2022.08.22
  • Accepted : 2022.10.22
  • Published : 2022.10.31
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Abstract

Laryngeal disease harms quality of life, and laryngoscopy is critical in identifying causative lesions. This study extracts and analyzes using radiomics quantitative features from the lesion in laryngoscopy images and will fit and validate a classifier for finding meaningful features. Searching the region of interest for lesions not classified by the YOLOv5 model, features are extracted with radionics. Selected the extracted features are through a combination of three feature selectors, and three estimator models. Through the selected features, trained and verified two classification models, Random Forest and Gradient Boosting, and found meaningful features. The combination of SFS, LASSO, and RF shows the highest performance with an accuracy of 0.90 and AUROC 0.96. Model using features to select by SFM, or RIDGE was low lower performance than other things. Classification of larynx lesions through radiomics looks effective. But it should use various feature selection methods and minimize data loss as losing color data.

Keywords

Acknowledgement

본 연구는 과학기술정보통신부 및 정보통신기획평가원의 대학ICT연구센터육성지원사업(IITP-2022-2017-0-01630)과 경기도의 경기도 지역협력연구센터 사업의 일환으로 수행하였음[GRRC-가천2020(B01), AI기반 의료영상분석].

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