Korean medicine Data Center 한의학의 임상현상을 과학적으로 규명하기 위한 체계적 통합 정보은행
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제목 콘볼루션 신경망(CNN)과 다양한 이미지 증강기법을 이용한 혀 영역 분할
등록일 2021-12-23 첨부파일
구분 학진
학술지 Journal of Biomedical Engineering Research
발표일 2021-10-25
저자 안일구, 이시우(한국한의학연구원) 외
In Korean medicine, tongue diagnosis is one of the important diagnostic methods for diagnosing abnormalities in the body. Representative features that are used in the tongue diagnosis include color, shape, texture, cracks, and tooth marks. When diagnosing a patient through these features, the diagnosis criteria may be different for each oriental medical doctor, and even the same person may have different diagnosis results depending on time and work environment. In order to overcome this problem, recent studies to automate and standardize tongue diagnosis using machine learning are continuing and the basic process of such a machine learning-based tongue diagnosis system is tongue segmentation. In this paper, image data is augmented based on the main tongue features, and backbones of various famous deep learning architecture models are used for automatic tongue segmentation. The experimental results show that the proposed augmentation technique improves the accuracy of tongue segmentation, and that automatic tongue segmentation can be performed with a high accuracy of 99.12%.

*원문신청: kdc@kiom.re.kr