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Deep Learning-Based Classification of Korean Basal Cell Carcinoma Using Convolutional Neural Network

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dc.date.accessioned2022-08-19T06:26:51Z-
dc.date.available2022-08-19T06:26:51Z-
dc.date.issued2019-01-
dc.identifier.issn2156-7018-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/189178-
dc.description.abstractA steady increase in incidence of basal cell carcinoma and other skin cancers has been reported in a number of epidemiologic studies conducted in Korea. In this study, we demonstrated a pipelined deep neural network-based model classifying the Korean basal cell carcinoma vs. normal nevus, using photographic images taken in various lighting, angles, and zoom to develop an early-detection diagnosis kits for malignant skin lesions. The original data set consisted of 1,200 photographic images-600 of basal cell carcinoma and 600 of normal nevus. Our study focused on the effectiveness of pipelining two different DNN-based models for this classification task. The performance of the pipeline was compared to the classification-only method based on four metrics: sensitivity, specificity, AUC, and accuracy. Various sizes of the training set were evaluated (n = 50, 100, 300, 600, 840) to assess the performance of each method. The classification-only model reached an AUC of .93. Closer inspection on the incorrectly-classified test points revealed that the model tended to mislabel noisy images. With the segmentation-classification model, the segmentation step allowed tighter cropping around the lesions, cutting out the distractions. Consequently, we found that for a given small dataset, the segmentation-classification model performed better than the classification-only model. This is a pioneering research in examining a deep learning system for the Korean-specific photographic images. While our study focused primarily on the Korean basal cell carcinoma, our data demonstrated the potential in cancer detection in the clinic where a sufficient number of quality training data are not available. Although we utilized noisy images in low resolution as input data, our findings demonstrated that the pipelining procedures can provide a promising system to deal with data having such limitations.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherAmerican Scientific Publishers-
dc.relation.isPartOfJOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleDeep Learning-Based Classification of Korean Basal Cell Carcinoma Using Convolutional Neural Network-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentOthers-
dc.contributor.googleauthorChung, Hyun Jung-
dc.contributor.googleauthorKim, Yoon Jae-
dc.contributor.googleauthorSong, Howon-
dc.contributor.googleauthorAhn, Sung Ku-
dc.contributor.googleauthorKim, Hyunggun-
dc.contributor.googleauthorHwang, Heon-
dc.identifier.doi10.1166/jmihi.2019.2560-
dc.relation.journalcodeJ03359-
dc.identifier.eissn2156-7026-
dc.identifier.urlhttps://www.ingentaconnect.com/content/asp/jmihi/2019/00000009/00000001/art00030;jsessionid=1ekib452uqoul.x-ic-live-03-
dc.subject.keywordBasal Cell Carcinoma-
dc.subject.keywordDeep Learning-
dc.subject.keywordConvolution Neural Network-
dc.subject.keywordSkin Cancer-
dc.subject.keywordSegmentation-
dc.citation.volume9-
dc.citation.number1-
dc.citation.startPage195-
dc.citation.endPage201-
dc.identifier.bibliographicCitationJOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, Vol.9(1) : 195-201, 2019-01-
Appears in Collections:
1. College of Medicine (의과대학) > Others (기타) > 1. Journal Papers

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