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다중 해상도를 이용한 딥러닝 기반 창상 분류 방법

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dc.contributor.author박경리-
dc.contributor.author김지훈-
dc.contributor.author김해문-
dc.contributor.author차지환-
dc.contributor.author유희진-
dc.contributor.author문영식-
dc.date.accessioned2023-09-04T05:44:06Z-
dc.date.available2023-09-04T05:44:06Z-
dc.date.issued2021-11-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/114959-
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.-
dc.format.extent5-
dc.language한국어-
dc.language.isoKOR-
dc.publisher대한전자공학회-
dc.title다중 해상도를 이용한 딥러닝 기반 창상 분류 방법-
dc.title.alternativeDeep Learning Based Wound Classification Method Using Multiple Resolutions-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.bibliographicCitation2021년 대한전자공학회 추계학술대회 논문집, pp 482 - 486-
dc.citation.title2021년 대한전자공학회 추계학술대회 논문집-
dc.citation.startPage482-
dc.citation.endPage486-
dc.type.docTypeProceeding-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassother-
dc.identifier.urlhttps://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11027634-
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