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Deep Learning-Based Evaluation of Ultrasound Images for Benign Skin Tumors

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dc.contributor.author오병호-
dc.date.accessioned2023-10-19T06:05:12Z-
dc.date.available2023-10-19T06:05:12Z-
dc.date.issued2023-08-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/196345-
dc.description.abstractIn this study, a combined convolutional neural network for the diagnosis of three benign skin tumors was designed, and its effectiveness was verified through quantitative and statistical analysis. To this end, 698 sonographic images were taken and diagnosed at the Department of Dermatology at Severance Hospital in Seoul, Korea, between 10 November 2017 and 17 January 2020. Through an empirical process, a convolutional neural network combining two structures, which consist of a residual structure and an attention-gated structure, was designed. Five-fold cross-validation was applied, and the train set for each fold was augmented by the Fast AutoAugment technique. As a result of training, for three benign skin tumors, an average accuracy of 95.87%, an average sensitivity of 90.10%, and an average specificity of 96.23% were derived. Also, through statistical analysis using a class activation map and physicians' findings, it was found that the judgment criteria of physicians and the trained combined convolutional neural network were similar. This study suggests that the model designed and trained in this study can be a diagnostic aid to assist physicians and enable more efficient and accurate diagnoses.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherMDPI-
dc.relation.isPartOfSENSORS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHDeep Learning*-
dc.subject.MESHHospitals-
dc.subject.MESHHumans-
dc.subject.MESHJudgment-
dc.subject.MESHSkin Neoplasms* / diagnostic imaging-
dc.subject.MESHUltrasonography-
dc.titleDeep Learning-Based Evaluation of Ultrasound Images for Benign Skin Tumors-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Dermatology (피부과학교실)-
dc.contributor.googleauthorHyunwoo Lee-
dc.contributor.googleauthorYerin Lee-
dc.contributor.googleauthorSeung-Won Jung-
dc.contributor.googleauthorSolam Lee-
dc.contributor.googleauthorByungho Oh-
dc.contributor.googleauthorSejung Yang-
dc.identifier.doi10.3390/s23177374-
dc.contributor.localIdA02367-
dc.relation.journalcodeJ03219-
dc.identifier.eissn1424-8220-
dc.identifier.pmid37687830-
dc.subject.keywordbenign skin tumor-
dc.subject.keywordclass activation map-
dc.subject.keywordconvolutional neural network-
dc.subject.keywordultrasound image-
dc.contributor.alternativeNameOh, Byung Ho-
dc.contributor.affiliatedAuthor오병호-
dc.citation.volume23-
dc.citation.number17-
dc.citation.startPage7374-
dc.identifier.bibliographicCitationSENSORS, Vol.23(17) : 7374, 2023-08-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Dermatology (피부과학교실) > 1. Journal Papers

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