• Title/Summary/Keyword: 피부질환

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Isolation and identification of culturable bacteria from human skin (배양가능한 피부세균의 분리 및 동정)

  • Bae, Young-Min
    • Journal of the Korean Applied Science and Technology
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    • v.37 no.6
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    • pp.1698-1705
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    • 2020

  • Bacteria were collected from the thumb surface of the twenty young adults that are 20 to 25 years old and cultured on the Luria-Bertani agar. The 16S rDNA of the cultured bacteria was amplified by polymerase chain reaction(PCR) and DNA sequence of the PCR products analyzed. Total 14 different bacterial species were identified by comparing their 16S rDNA sequence with the data in genbank. It appears that each individual has 2.5 different bacterial species in average. Staphylococcal species were the most abundant among the identified bacteria and Micrococcus luteus was the second. Staphylococcal species were isolated at similar frequency between male and female donors but Micrococcus luteus was isolated more frequently from female than male donors. The result obtained in this study might be useful in research of dermatic diseases, searching for new drugs for those diseases and development of new cosmetics.

  • https://doi.org/10.12925/jkocs.2020.37.6.1698 인용 PDF KSCI

Skin Disease Classification Technique Based on Convolutional Neural Network Using Deep Metric Learning (Deep Metric Learning을 활용한 합성곱 신경망 기반의 피부질환 분류 기술)

  • Kim, Kang Min;Kim, Pan-Koo;Chun, Chanjun
    • Smart Media Journal
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    • v.10 no.4
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    • pp.45-54
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    • 2021

  • The skin is the body's first line of defense against external infection. When a skin disease strikes, the skin's protective role is compromised, necessitating quick diagnosis and treatment. Recently, as artificial intelligence has advanced, research for technical applications has been done in a variety of sectors, including dermatology, to reduce the rate of misdiagnosis and obtain quick treatment using artificial intelligence. Although previous studies have diagnosed skin diseases with low incidence, this paper proposes a method to classify common illnesses such as warts and corns using a convolutional neural network. The data set used consists of 3 classes and 2,515 images, but there is a problem of lack of training data and class imbalance. We analyzed the performance using a deep metric loss function and a cross-entropy loss function to train the model. When comparing that in terms of accuracy, recall, F1 score, and accuracy, the former performed better.

  • https://doi.org/10.30693/SMJ.2021.10.4.45 인용 PDF KSCI