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폐 결절 검출을 위한 합성곱 신경망의 성능 개선

DC FieldValueLanguage
dc.contributor.author유선국-
dc.contributor.author장원석-
dc.date.accessioned2018-07-20T11:57:36Z-
dc.date.available2018-07-20T11:57:36Z-
dc.date.issued2017-
dc.identifier.issn1229-0807-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/161552-
dc.description.abstractEarly detection of the pulmonary nodule is important for diagnosis and treatment of lung cancer. Recently, CT has been used as a screening tool for lung nodule detection. And, it has been reported that computer aided detection(CAD) systems can improve the accuracy of the radiologist in detection nodules on CT scan. The previous study has been proposed a method using Convolutional Neural Network(CNN) in Lung CAD system. But the proposed model has a limitation in accuracy due to its sparse layer structure. Therefore, we propose a Deep Convolutional Neural Network to overcome this limitation. The model proposed in this work is consist of 14 layers including 8 convolutional layers and 4 fully connected layers. The CNN model is trained and tested with 61,404 regions-of-interest (ROIs) patches of lung image including 39,760 nodules and 21,644 non-nodules extracted from the Lung Image Database Consortium(LIDC) dataset. We could obtain the classification accuracy of 91.79% with the CNN model presented in this work. To prevent overfitting, we trained the model with Augmented Dataset and regularization term in the cost function. With L1, L2 regularization at Training process, we obtained 92.39%, 92.52% of accuracy respectively. And we obtained 93.52% with data augmentation. In conclusion, we could obtain the accuracy of 93.75% with L2 Regularization and Data Augmentation.-
dc.description.statementOfResponsibilityopen-
dc.languageKorean-
dc.publisherThe Korea Society of Medical and Biological Engineering-
dc.relation.isPartOfJournal of Biomedical Engineering Research (의공학회지)-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.title폐 결절 검출을 위한 합성곱 신경망의 성능 개선-
dc.title.alternativePerformance Improvement of Convolutional Neural Network for Pulmonary Nodule Detection-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine-
dc.contributor.departmentDept. of Medical Engineering-
dc.contributor.googleauthor김한웅-
dc.contributor.googleauthor김병남-
dc.contributor.googleauthor이지은-
dc.contributor.googleauthor장원석-
dc.contributor.googleauthor유선국-
dc.identifier.doi10.9718/JBER.2017.38.5.237-
dc.contributor.localIdA02471-
dc.contributor.localIdA04793-
dc.relation.journalcodeJ01263-
dc.subject.keywordPulmonary nodule detection-
dc.subject.keywordConvolutional Neural Network-
dc.subject.keywordMachine learning-
dc.contributor.alternativeNameYoo, Sun Kook-
dc.contributor.alternativeNameChang, Won Seok-
dc.contributor.affiliatedAuthorYoo, Sun Kook-
dc.contributor.affiliatedAuthorChang, Won Seok-
dc.citation.volume38-
dc.citation.number5-
dc.citation.startPage237-
dc.citation.endPage241-
dc.identifier.bibliographicCitationJournal of Biomedical Engineering Research (의공학회지), Vol.38(5) : 237-241, 2017-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers

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