Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 문영식 | - |
dc.date.accessioned | 2022-04-01T02:38:04Z | - |
dc.date.available | 2022-04-01T02:38:04Z | - |
dc.date.issued | 2021-11 | - |
dc.identifier.citation | 대한전자공학회 학술대회. 2021-11 2021(11):388-391 | en_US |
dc.identifier.uri | https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11027604 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/169624 | - |
dc.description.abstract | Skin lesions result from abnormal proliferation of cells. Skin lesions have high rate of misdiagnosis due to a wide variety of forms. Recently, a deep learning-based skin lesion classification methods have been proposed but they tend to misclassify lesions with unclear skin lesion boundaries. In this paper, we propose a method for classifying skin lesions using deep neural network with area information. As a result, we show that the performance of our method is improved by 1.7~2.43 in terms of F1-score, compared to the previous methods. | en_US |
dc.description.sponsorship | 본 연구는 과학기술정통신부 및 정보통신기획평가원 의 SW중심대학지원사업의 연구결과로 수행되었으며 (2018-0-00192) 연구지원에 감사드립니다. 이 논문은 2021년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임 (No.2020-0-01343, 인공지능융합연구센터 지원(한양대학교 ERICA)) | en_US |
dc.language.iso | ko_KR | en_US |
dc.publisher | 대한전자공학회 | en_US |
dc.title | 딥러닝 기반 영역 정보를 활용한 피부 병변 분류 방법 | en_US |
dc.title.alternative | Deep Learning Based Skin Lesion Classification Using Area Information | en_US |
dc.type | Article | en_US |
dc.relation.page | 388-391 | - |
dc.contributor.googleauthor | 서, 지원 | - |
dc.contributor.googleauthor | 김, 지훈 | - |
dc.contributor.googleauthor | 김, 해문 | - |
dc.contributor.googleauthor | 박, 경리 | - |
dc.contributor.googleauthor | 강, 경원 | - |
dc.contributor.googleauthor | 문, 영식 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF COMPUTING[E] | - |
dc.sector.department | SCHOOL OF COMPUTER SCIENCE | - |
dc.identifier.pid | ysmoon | - |
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