DEXA에서 딥러닝 기반의 척골 및 요골 자동 분할 모델
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김영재 | - |
dc.contributor.author | 박성진 | - |
dc.contributor.author | 김경래 | - |
dc.contributor.author | 김광기 | - |
dc.date.available | 2020-02-27T13:41:59Z | - |
dc.date.created | 2020-02-12 | - |
dc.date.issued | 2018-12 | - |
dc.identifier.issn | 1229-7771 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/4584 | - |
dc.description.abstract | The purpose of this study was to train a model for the ulna and radius bone segmentation based on Convolutional Neural Networks and to verify the segmentation model. The data consisted of 840 training data, 210 tuning data, and 200 verification data. The learningmodel for the ulna and radius bone bwas based on U-Net (19 convolutional and 8 maximum pooling) and trained with 8 batch sizes, 0.0001 learning rate, and 200 epochs. As a result, the average sensitivity of the training data was 0.998, the specificity was 0.972, the accuracy was 0.979, and the Dice’s similarity coefficient was 0.968. In the validation data, the average sensitivity was 0.961, specificity was 0.978, accuracy was 0.972, and Dice’s similarity coefficient was 0.961. The performance of deep convolutional neural network based models for the segmentation was good for ulna and radius bone. | - |
dc.language | 한국어 | - |
dc.language.iso | ko | - |
dc.publisher | 한국멀티미디어학회 | - |
dc.relation.isPartOf | 멀티미디어학회논문지 | - |
dc.title | DEXA에서 딥러닝 기반의 척골 및 요골 자동 분할 모델 | - |
dc.title.alternative | Automated Ulna and Radius Segmentation model based on Deep Learning on DEXA | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 2 | - |
dc.identifier.doi | 10.9717/kmms.2018.21.12.1407 | - |
dc.identifier.bibliographicCitation | 멀티미디어학회논문지, v.21, no.12, pp.1407 - 1416 | - |
dc.identifier.kciid | ART002424865 | - |
dc.description.isOpenAccess | N | - |
dc.citation.endPage | 1416 | - |
dc.citation.startPage | 1407 | - |
dc.citation.title | 멀티미디어학회논문지 | - |
dc.citation.volume | 21 | - |
dc.citation.number | 12 | - |
dc.contributor.affiliatedAuthor | 김영재 | - |
dc.contributor.affiliatedAuthor | 김경래 | - |
dc.contributor.affiliatedAuthor | 김광기 | - |
dc.subject.keywordAuthor | DEXA | - |
dc.subject.keywordAuthor | Deep Learning | - |
dc.subject.keywordAuthor | Convolutional Neural Network | - |
dc.subject.keywordAuthor | U-Net | - |
dc.subject.keywordAuthor | Segmentation | - |
dc.description.journalRegisteredClass | kci | - |
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