• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1462-1468
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• 2022

Among the data used for the diagnosis of calf disease, feces play an important role in disease diagnosis. In the image of calf feces, the health status can be known by the shape, color, and texture. For the fecal image that can identify the health status, data of 207 normal calves and 158 calves with diarrhea were pre-processed according to fecal status and used. In this paper, images of fecal variables are detected among the collected calf data and images are trained by applying GLCM-CNN, which combines the properties of CNN and GLCM, on a dataset containing disease symptoms using convolutional network technology. There was a significant difference between CNN's 89.9% accuracy and GLCM-CNN, which showed 91.7% accuracy, and GLCM-CNN showed a high accuracy of 1.8%.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.35-40
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• 2022

In this paper, we propose a method of constructing clinical knowledge with clinical concept relations and predicting diseases based on a deep learning model to support clinical decision-making. Clinical terms in UMLS(Unified Medical Language System) and cancer-related medical knowledge are classified into five categories. Medical related documents in Wikipedia are extracted using the classified clinical terms. Clinical concept relations are established by matching the extracted medical related documents with the extracted clinical terms. After deep learning using clinical knowledge, a disease is predicted based on medical terms expressed in a query. Thereafter, medical terms related to the predicted disease are selected as an extended query for clinical document retrieval. To validate our method, we have experimented on TREC Clinical Decision Support (CDS) and TREC Precision Medicine (PM) test collections.

• Journal of Oral Medicine and Pain
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• v.26 no.3
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• pp.189-204
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• 2001

본 연구는, 기존의 나열식 분류체계의 문제점을 극복할 수 있는 GRAIL을 이용하여 두경부의 해부학적 구조물들 및 구강 악안면 영역의 주요 질병들과 관련된 치의학 개념들의 모델을 구축한 뒤, 완성된 치의학 개념 모델이 두경부의 해부학적 구조물들 및 구강 악안면 영역의 주요 질병들을 잘 표현할 수 있는지와 기존의 GRAIL 모델이 지닌 특징에 잘 부합하는지를 평가하고자 시행되었다. 서울대학교 치과병원 내원 환자 중 포괄적인 치과 치료 병력을 지닌 환자 150명의 치과 의무기록을 내용별로 분석하고, 각종 치의학 교과서와 기존의 의학용어 분류체계에서도 모델 구축에 필요한 치의학 용어를 선택하였다. 이들 자료를 바탕으로, GRAIL 모델 구축을 진행하고 구축된 모델을 평가할 수 있는 소프트웨어 프로그램인 'KnoME'에서 치의학 개념 모델을 구축하고 평가하여, 다음과 같은 결론을 얻었다. 1. 환자 150명의 치과 의무기록을 내용별로 분석한 결과, 우선적으로 모델 구축이 필요한 치의학 용어로는, 해부학적 구조물의 경우 치아, 치은, 악관절, 입술, 턱, 혀 등의 순서로 나타났으며, 구강악안면 영역의 병소에서는 치아 우식증, 치주염, 치은염, 악관절 장애, 매복 지치, 치경부 마모 등의 순서로 나타났다. 2. GRAIL을 이용하여 치아, 치주조직, 구강점막조직, 치아 우식증, 치수 및 치근단 병소, 치주질환, 구강점막질환의 모델 구축을 시행한 결과, 치의학 개념간의 다양한 관계가 대다수 잘 표현되었다. 그러나, 구강 악안면 영역의 해부학적 구조물에 대한 공간 정의의 한계성과 구강 악안면 질환의 진행 양상에 있어서 표현의 어려움이 관찰되었다. 이러한 부분은 GRAIL을 치의학 분야에 적용할 때, 극복해야 할 한계로 나타났다. 3. 치의학 개념들에 관한 다양한 질의를 시행한 후 그 응답 내용을 평가한 결과, 완성된 모델 내에서 치의학 개념의 자동적인 분류가 이루어 졌으며, 다양한 목적의 검색이 가능하였다. 이와 같은 사실로 미루어 보아서, 완성된 모델은 기존의 GRAIL 모델의 특성에 잘 부합되는 것으로 생각되었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.796-801
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• 2009

In medical practice, the diagnosis or prediction models requiring complicated computations are not widely recognized due to difficulty in interpreting the course of reasoning and the complexity of computations. Medical personnel have used the nomograms which are a graphical representation for numerical relationships that enables to easily compute a complicated function without help of computation machines. It has been widely paid attention in diagnosing diseases or predicting the progress of diseases. A nomogram is constructed from a set of clinical data which contain various attributes such as symptoms, lab experiment results, therapy history, progress of diseases or identification of diseases. It is of importance to select effective ones from available attributes, sometimes along with parameters accompanying the attributes. This paper introduces a nomogram construction method that uses a naive Bayesian technique to construct a nomogram as well as a genetic algorithm to select effective attributes and parameters. The proposed method has been applied to the construction of a nomogram for a real clinical data set.

• Smart Media Journal
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• v.12 no.7
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• pp.9-17
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• 2023

Plant diseases and pests affect the growth of various plants, so it is very important to identify pests at an early stage. Although many machine learning (ML) models have already been used for the inspection and classification of plant pests, advances in deep learning (DL), a subset of machine learning, have led to many advances in this field of research. In this study, disease and pest inspection of abnormal crops and maturity classification were performed for normal crops using YOLOX detector and MobileNet classifier. Through this method, various plant pest features can be effectively extracted. For the experiment, image datasets of various resolutions related to strawberries, peppers, and tomatoes were prepared and used for plant pest classification. According to the experimental results, it was confirmed that the average test accuracy was 84% and the maturity classification accuracy was 83.91% in images with complex background conditions. This model was able to effectively detect 6 diseases of 3 plants and classify the maturity of each plant in natural conditions.

• Journal of Korea Game Society
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• v.20 no.1
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• pp.83-92
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• 2020

The WHO has defined gaming disorder as a disorder, and there are arguments for and against it from different perspectives. In response, this paper tried to identify the disease model, psychosocial, and cultural perspective for complementing them with an interdisciplinary attitude. First, universal prevention should be provided for general game users to get literacy. Second, selective prevention should be provided for a potentially risky group to find out the alternative activity. Finally, indicated prevention should be provided for a risky group to be treated.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.614-616
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• 2021

The world faces difficulties in terms of eye care, including treatment, quality of prevention, vision rehabilitation services, and scarcity of trained eye care experts. However, it is difficult to develop a method for classifying various ocular diseases because the existing dataset for retinal image disclosure does not consist of various diseases found in clinical practice. We propose a method for classifying ocular diseases using the Retinal Fundus Multi-disease Image Dataset (RFMiD), a dataset published in the ISBI-2021 challenge. Our goal is to develop a robust and generalizable model for screening retinal images into normal and abnormal categories. The performance of the proposed model shows a value of 0.9782 for the test dataset as an area under the curve (AUC) score.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.323-330
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• 2008

Since complex diseases are caused by interactions of multiple genes, traditional statistical methods are limited in its power to predict the onset of a complex disease. Recently new approaches using machine learning techniques are introduced. Neural nets are a suitable model to find patterns in complex data. When large amount of data are fed into a neural net, however, it takes a long time for learning and finding patterns. In this study we suggest a new model that combines the set association, which is a statistical technique to find important SNPs associated with complex diseases, and neural network. We experiment with SNP data related to asthma to test the effectiveness of our model. Our model shows higher prediction accuracy and shorter execution time than neural net only. We expect our model can be used effectively to predict the onset of other complex diseases.

• Smart Media Journal
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• v.13 no.8
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• pp.49-57
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• 2024

In this paper, we propose an algorithm that can recommend personalized health functional foods considering diseases due to the high interest in health functional foods and the development of machine learning as society enters an aging phase. By applying the KNN algorithm, we presented a foundational framework for a platform for personalized health functional food recommendations through disease analysis, matching techniques of publicly available health functional food information, and national public data. To ensure reliable matching between diseases and health functional foods, we analyzed correlations, assessed the appropriateness and accuracy of variables for enhancing the KNN algorithm, and derived improvement directions for the proposed system through the improvement of learning models and information to be disclosed in the future.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.688-690
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• 2003

본 논문에서는 다해상도 2차원 실루엣 영상들을 이용하여 3차원 모델간의 형상 유사성을 비교하기 위한 방법을 제안한다. 제안 시스템은 포즈 정규화 모듈, 유사성 계산 모듈, 3차원 시각화 모듈로 구성된다. 형상 비교를 위해서 먼저, 3차원 인체 장기 모델을 입력으로 받아서 정규화를 수행하고, 다해상도 깊이맵을 획득한다. 이어서 유사성 비교를 위해 실루엣 영상을 추출한 후, 유사도 측정을 위해 시그니쳐를 측도로 사용한다. 최종적으로 계산된 결과들은 3차원 글리프 및 컬러 코딩을 이용하여 시각화된다. 본 논문에서 제시한 3차원 형상 비교 시스템은 전처리 단계에서의 정규화 수행을 통하여 스케일 및 회전 변환에 불변하는 특성을 보인다. 그리고 다양한 레벨의 깊이맵을 형상 비교에 사용하여 다해상도 기반의 유사성 평가를 지원하며, 평가 계산 속도와 정확성간의 유연성을 제공한다. 또한 3차원 히스토그램. 3차윈 글리프. 컬러 코딩 시각화 기법들과 2차원 실루엣 피킹 인터페이스를 통하여 인체 장기 모델간의 정량적 형상 차이를 사용자가 직관적으로 평가할 수 있도록 한다. 본 시스템은 차후 데이터베이스를 이용한 원격 진료 시스템에서의 질병 진단, 추적 관찰. 치료계획 등에 활용될 수 있을 것이다.