• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.555-560
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• 2021

Recently, as people's interest in facial skin beauty has increased, research on skin disease recognition for facial skin beauty is being conducted by using deep learning. These studies recognized a variety of skin diseases, including acne. Existing studies can recognize only the single skin diseases, but skin diseases that occur on the face can enact in a more diverse and complex manner. Therefore, in this paper, complex skin diseases such as acne, blackheads, freckles, age spots, normal skin, and whiteheads are identified using the Inception-ResNet V2 deep learning mode with multi-label classification. The accuracy was 98.8%, hamming loss was 0.003, and precision, recall, F1-Score achieved 96.6% or more for each single class.

• Journal of Korea Multimedia Society
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• v.23 no.10
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• pp.1250-1257
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• 2020

The early detection of diseases is important in agriculture because diseases are major threats of reducing crop yield for farmers. The shape and color of plant leaf are changed differently according to the disease. So we can detect and estimate the disease by inspecting the visual feature in leaf. This study presents a vision-based leaf classification method for detecting the diseases of tomato crop. ResNet-50 model was used to extract the visual feature in leaf and classify the disease of tomato crop, since the model showed the higher accuracy than the other ResNet models with different depths. We propose a new ensemble approach using several DCNN classifiers that have the same structure but have been trained at different ranges in the DCNN layers. Experimental result achieved accuracy of 97.19% for PlantVillage dataset. It validates that the proposed method effectively classify the disease of tomato crop.

• Tuberculosis and Respiratory Diseases
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• v.81 no.3
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• pp.256-257
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• 2018

• The Journal of Internal Korean Medicine
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• v.31 no.1
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• pp.1-10
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• 2010

Objectives : It is necessary that the international classification of diseases (ICD) be examined in order to comprise the third revision of the Korean Classification of Disease on Oriental Medicine (KCD-OM) and disease classification in the oriental internal medicine field. It is essential that the selection, classification and definition of disease and pattern names of oriental concepts in internal medicine be clear. Since 2008, the fifth revision of the Korean Classification of Disease (KCD-5) has been used in Korea. It was required to use the reference classification from the Oriental medicine area based on the ICD-10. Methods : In this review, the necessity for, meaning of and content of the third revision are briefly described. The ICD system was reviewed and KCD-OM was reconstructed. How diagnosis in the oriental internal medicine area had changed is discussed. Review and Results : In 1973, the disease classification of oriental medicine was established the basis on the contents of Dongeuibogam. It was irrespective of the ICD. As to the classification system in the Oriental internal medicine field, systemic disease was comprised of wind, cold, warm, wet, dryness, heat, spirit, ki, blood, phlegm and retained fluid, consumptive disease, etc. Diseases of internal medicine comprised a system according to the five viscera and the six internal organs and followed the classification system of Dongeuibogam. The first and second revisions were of the classification system based on the curriculum in 1979 and 1995. In 1979, in the first revision, geriatric disease and idiopathic types of disease were deleted, and skin disease was included among surgery diseases. This classification was expanded to 792 small classification items and 1,535 detailed classification items to the dozen disease classes. In 1995, in the second revision, it was adjusted to 644 small classes and 1,784 detailed classification items in the dozen disease classes. KCD-OM3 did KCD from this basis. It added and comprised the oriental medical doctor's concept names of diseases considering the special conditions in Korea. KCD-OM3 examined the KCD-OMsecond revised edition (1994). It improved the duplex classification, improper classifications, etc. It is difficult for us to separate the disease names and pattern names in oriental medicine. We added to the U code and made one classification system. By considering the special conditions in Korea, 169 codes (83 disease name codes, 86 pattern name codes) became the pre-existence classification and links among 306 U codes of KCD-OM3. 137 codes were newly added in the third revision. U code added 3 domains. These are composed of the disease name (U20-U33, 97 codes), the disease pattern name (U50-U79, 191 codes) and the constitution pattern name of each disease (U95-U98, 18 codes). Conclusion : The introduction of KCD-OM3 conforms to the diagnostic system by which oriental medical doctors examine classes used with the basic structure of the reference classification of WHO and raises the clinical study and academic activity of the Korean oriental medicine and makes the production of all kinds of nation statistical indices possible. The introduction of KCD-OM3 promotes the diagnostic system by which doctors of Oriental medicine examine classes using the association with KCD-5. It will raise the smoothness and efficiency of oriental medical treatment payments in the health insurance, automobile insurance, industrial accident compensation insurance, etc. In addition, internationally, the eleventh revision work of the ICD has been initiated. It needs to consider incorporating into the International Classification of Diseases some of every country's traditional medicine.

• Journal of Digestive Cancer Reports
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• v.9 no.2
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• pp.57-59
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• 2021

Carcinoid tumor is called as neuroendocrine tumor and is classified into neuroendocrine tumor Grade 1, neuroendocrine tumor Grade 2, and neuroendocrine carcinoma based on the differentiation of tumors. Recently, the incidence of rectal carcinoid tumor has been increasing probably due to the increased interest on screening colonoscopy and the advancement of endoscopic imaging technology. As the rectal carcinoid shows a wide range of clinical characteristics such as metastasis and long-term prognosis depending on the size and histologic features, it is a challenge to give a consistent diagnostic code in patients with the rectal carcinoid. If the rectal carcinoid tumor is less than 1 cm in size, it can be given as the code of definite malignancy or the code of uncertain malignant potential according to International Classification of Diseases for Oncology (ICD-O) by World Health Organization (WHO). Because patients get different amount of benefit from the insurance company based on different diagnostic codes, this inconsistent coding system has caused a significant confusion in the clinical practice. In 2019, WHO updated ICD-O and Statistics Korea subsequently changed Korean Standard Classification of Diseases (KCD) including the code of rectal carcinoid tumors. This review will summarize what has been changed in recent ICD-O and KCD system regarding the rectal carcinoid tumor and surmise its clinical implication.

• Korean Journal of Veterinary Service
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• v.28 no.1
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• pp.49-69
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• 2005

More than 30 years have elapsed since the first report of the International Committee on Taxonomy of Viruses (ICTV) was published in 1971. Since that publication, the ICTV recognizes about 1,550 virus species, but some 30,000 virus strains and isolates are being tracked by virologists in different fields of biology. The ICTV is the 'international court' of experts that rules on names and relationships of all virus, but only to the level of species. Virus taxonomy is changing rapidly, with changes ranging from the trivial(use of italics for species names) to profound reorganization driven by the explosion of sequence information. The universal system of viral taxonomy now accepts Linnean-like classification at the levels of order, family, subfamily, genus, and species. The suffix '-virales' identifies an order, Families are identified by the suffix '-viridae' subfamilies are identified by the suffix '-virinae', and genera are identified by the suffix '-virus'. The importance of distinguishing subspecies, strains, and isolates in vaccine development, diagnostics, etc. is recognized, but these lower levels are not formally classified by ICTV. This paper mainly introduces taxonomy and classification of animal viruses on the basis of the seventh report of the ICTV edited by Van Regenmortal et al. in 2000.

• Tuberculosis and Respiratory Diseases
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• v.43 no.3
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• pp.297-307
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• 1996

• Tuberculosis and Respiratory Diseases
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• v.81 no.4
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• pp.347-348
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• 2018

• Journal of Environmental Science International
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• v.19 no.6
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• pp.715-735
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• 2010

This study is designed to assess the priority order of the chemicals to cause to generate occupational diseases in order to understand the fundamental data required for the preparation of health protective measure for the workers dealing with chemicals. The 41 types of 51 ones of chemicals to cause to generate the national occupational diseases were selected as the study objects by understanding their domestic use or not, and their occupational diseases' occurrence or not among 110,608 types of domestic and overseas chemicals. To assess their priority order the sum of scores was acquired by understanding the actually classified condition based on a perfect score of physical riskiness(90points) and health toxicity(92points) as a classification standard by GHS, the priority order on GHS riskiness assessment, GHS toxicity assessment, GHS toxic xriskiness assessment(sum of riskiness plus toxicity) was assessed by multiplying each result by each weight of occupational disease's occurrence. The high ranking 5 items of chemicals for GHS riskiness assessment were turned out to be urethane, copper, chlorine, manganese, and thiomersal by order. Besides as a result of GHS toxicity assessment the top fives were assessed to be aluminum, iron oxide, manganese, copper, and cadium(Metal) by order. On the other hand, GHS toxicity riskiness assessment showed that the top fives were assessed to be copper, urethane, iron oxide, chlorine and phenanthrene by order. As there is no material or many uncertain details for physical riskiness or health toxicity by GHS classification though such materials caused to generate the national occupational diseases, it is very urgent to prepare its countermeasure based on the forementioned in order to protect the workers handling or being exposed to chemicals from health.

• Journal of Preventive Medicine and Public Health
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• v.32 no.4
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• pp.538-545
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• 1999

Objective : To compare the predictive power of International Classification of Diseases 10th Edition(ICD-10) based International Classification of Diseases based Injury Severity Score(ICISS) with Trauma and Injury Severity Score(TRISS) and International Classification of Diseases 9th Edition Clinical Modification(ICD-9CM) based ICISS in the injury severity measure. Methods : ICD-10 version of Survival Risk Ratios(SRRs) was derived from 47,750 trauma patients from 35 Emergency Centers for 1 year. The predictive power of TRISS, the ICD-9CM based ICISS and ICD-10 based ICISS were compared in a group of 367 severely injured patients admitted to two university hospitals. The predictive power was compared by using the measures of discrimination(disparity, sensitivity, specificity, misclassification rates, and ROC curve analysis) and calibration(Hosmer-Lemeshow goodness-of-fit statistics), all calculated by logistic regression procedure. Results : ICD-10 based ICISS showed a lower performance than TRISS and ICD-9CM based ICISS. When age and Revised Trauma Score(RTS) were incorporated into the survival probability model, however, ICD-10 based ICISS full model showed a similar predictive power compared with TRISS and ICD-9CM based ICISS full model. ICD-10 based ICISS had some disadvantages in predicting outcomes among patients with intracranial injuries. However, such weakness was largely compensated by incorporating age and RTS in the model. Conclusions : The ICISS methodology can be extended to ICD-10 horizon as a standard injury severity measure in the place of TRISS, especially when age and RTS were incorporated in the model. In patients with intracranial injuries, the predictive power of ICD-10 based ICISS was relatively low because of differences in the classifying system between ICD-10 and ICD-9CM.