지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
Recently, artificial intelligence for diagnosis system of obstetric diseases have been actively studied. Arti- ficial 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 com- pared 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 effec- tively excluding unread cervical imaging and ultimately reducing errors in artificial intelligence diagnosis.
목차
등록된 정보가 없습니다.
참고문헌 (20)
참고문헌 신청
최현주 / 2011 / 자궁경부암 진단을 위한 3차원 세포핵 질감 특성값 유의성 평가에 관한 연구 / 한국컴퓨터정보학회논문지 16(10):83~92
http://www.amc.seoul.kr/asan/healthinfo/disease/diseaseDetail. do?contentId=31818
https://www.cancer.go.kr/lay1/program/S1T211C223/cancer/view.do?cancer_seq=4877
Alyafeai Z / 2020 / A fully-automated deep learning pipeline for cervical cancer classification / Expert Systems with Applications :83~86
Chandran V / 2021 / Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images / BioMed Research International :83~86
http://www.amc.seoul.kr/asan/healthinfo/disease/diseaseDetail. do?contentId=31818
https://www.cancer.go.kr/lay1/program/S1T211C223/cancer/view.do?cancer_seq=4877
Alyafeai Z / 2020 / A fully-automated deep learning pipeline for cervical cancer classification / Expert Systems with Applications :83~86
Chandran V / 2021 / Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images / BioMed Research International :83~86
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