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dc.contributor.author문영식-
dc.date.accessioned2022-04-01T02:38:39Z-
dc.date.available2022-04-01T02:38:39Z-
dc.date.issued2021-11-
dc.identifier.citation대한전자공학회 학술대회. 2021-11 2021(11):482-486en_US
dc.identifier.urihttps://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11027634-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/169626-
dc.description.abstractIn medical data, the distance between the wound and the imaging device is not constant, so there is a problem that the deep learning model becomes sensitive to changes in resolution. To solve this problem, in this paper, we propose a network that is robust to resolution changes through multiple resolution input. Experimentally, the proposed method is more robust to resolution changes than the existing method, and shows 1.48% higher performance.en_US
dc.description.sponsorship본 연구는 과학기술정통신부 및 정보통신기획평가원의 SW 중심대학지원사업의 연구결과로 수행되었으며 (2018-0-00192) 연구 지원에 감사드립니다. 이 논문은 2021년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임 (No.2020-0-01343, 인공지능융합연구센터지원(한양대학교 ERICA))en_US
dc.language.isoko_KRen_US
dc.publisher대한전자공학회en_US
dc.title다중 해상도를 이용한 딥러닝 기반 창상 분류 방법en_US
dc.title.alternativeDeep Learning Based Wound Classification Method Using Multiple Resolutionsen_US
dc.typeArticleen_US
dc.relation.page482-486-
dc.contributor.googleauthor박, 경리-
dc.contributor.googleauthor김, 지훈-
dc.contributor.googleauthor김, 해문-
dc.contributor.googleauthor차, 지환-
dc.contributor.googleauthor유, 희진-
dc.contributor.googleauthor문, 영식-
dc.sector.campusE-
dc.sector.daehakCOLLEGE OF COMPUTING[E]-
dc.sector.departmentSCHOOL OF COMPUTER SCIENCE-
dc.identifier.pidysmoon-
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