딥러닝 기반 영역 정보를 활용한 피부 병변 분류 방법
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 서지원 | - |
dc.contributor.author | 김지훈 | - |
dc.contributor.author | 김해문 | - |
dc.contributor.author | 박경리 | - |
dc.contributor.author | 강경원 | - |
dc.contributor.author | 문영식 | - |
dc.date.accessioned | 2023-09-04T05:44:05Z | - |
dc.date.available | 2023-09-04T05:44:05Z | - |
dc.date.issued | 2021-11 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/114958 | - |
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. | - |
dc.format.extent | 4 | - |
dc.language | 한국어 | - |
dc.language.iso | KOR | - |
dc.publisher | 대한전자공학회 | - |
dc.title | 딥러닝 기반 영역 정보를 활용한 피부 병변 분류 방법 | - |
dc.title.alternative | Deep Learning Based Skin Lesion Classification Using Area Information | - |
dc.type | Article | - |
dc.publisher.location | 대한민국 | - |
dc.identifier.bibliographicCitation | 2021년 대한전자공학회 추계학술대회 논문집, pp 388 - 391 | - |
dc.citation.title | 2021년 대한전자공학회 추계학술대회 논문집 | - |
dc.citation.startPage | 388 | - |
dc.citation.endPage | 391 | - |
dc.type.docType | Proceeding | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | other | - |
dc.identifier.url | https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11027604 | - |
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