• 대한한의정보학회지
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• 제18권1호
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• pp.1-63
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• 2012

Objective : "Huatuoxuanmenneizhaotu" is a Huatuo's about 5~6 century works are estimated to be voted for. Scored the first Anatomical Pictures. Expand your knowledge on the anatomy of the later set the foundation. This books is part of two volumes, which is largely divided into six parts. We have concern on the content and features. Method : Through "Huatuoxuanmenneizhaotu" text translation, we will try to categorize two ways : classifying 1) The first volume of Chapter 1, Pictures 2) The second volume divided into four parts, Chapter 2, Viscera Disease and Chapter 3, Viscera metastasize and Chapter 4, Viscera and Bowel each for metastasize, explained. Result : In consideration against Disease symptom classification, Medicine classification, processing of medicinals examine. Viscera Disease symptom each wind pattern(風證), qi pattern(氣證), heat pattern(熱證), cold pattern(冷證), deficiency pattern(虛證) was classified as. Same method were explained as Viscera into Viscera, as Viscera into Bowel. Viscera Disease also not mentioned in the Bowel Disease symptoms were found to be viewed. Conclusion : These results explain "HuatuoXuanmenNeizhaotu" were really diverse and various.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.97-99
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• 2021

최근 고령화 사회가 지속됨에 따라, 치매(Dementia)에 대한 관심이 높아지고 있다. 그 중에서 알츠하이머병(Alzheimer's disease)는 전체 치매 환자의 50~60%로 가장 많은 비율을 차지하는 퇴행성 뇌질환으로, 현재 의료계에선 알츠하이머병에 대한 명확한 예방법 및 치료법에 대해 내놓지 못하고 있으며, 치매 발병 전 조기 치료 및 조기 예방법에 대한 중요성이 강조되고 있다. 본 논문에서는 정상인과 알츠하이머병에 걸린 환자의 MRI 데이터셋을 활용하여 컨볼루션 신경망을 중심으로 여러 가지 활성화 함수를 접목시켜, 가장 효율적인 활성화 함수를 찾고자 한다. 또한 알츠하이머 치매분류 모델링을 통해 향후 의료분야에 적합한 치매 구분 모델링으로 활용하고자 한다.

• The Plant Pathology Journal
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• 제39권4호
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• pp.319-334
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• 2023

Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.

• 산업경영시스템학회지
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• 제44권3호
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• pp.33-38
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• 2021

Recently, transfer learning techniques with a base convolutional neural network (CNN) model have widely gained acceptance in early detection and classification of crop diseases to increase agricultural productivity with reducing disease spread. The transfer learning techniques based classifiers generally achieve over 90% of classification accuracy for crop diseases using dataset of crop leaf images (e.g., PlantVillage dataset), but they have ability to classify only the pre-trained diseases. This paper provides with an evaluation scheme on selecting an effective base CNN model for crop disease transfer learning with regard to the accuracy of trained target crops as well as of untrained target crops. First, we present transfer learning models called CDC (crop disease classification) architecture including widely used base (pre-trained) CNN models. We evaluate each performance of seven base CNN models for four untrained crops. The results of performance evaluation show that the DenseNet201 is one of the best base CNN models.

• 한국콘텐츠학회논문지
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• 제22권3호
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• pp.354-360
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• 2022

대사성 질환은 국내의 경우 유병률이 26%에 이르는 질환으로 복부비만, 고혈압, 공복혈당장애, 고중성지방, 낮은 HDL 콜레스테롤 5가지 상태 중 3가지를 동시에 가진 상태를 말한다. 본 논문은 농촌진흥청의 소비자패널 데이터와 건강보험공단의 진료 데이터를 연계하여 식품 소비 특성을 통해 대사성 질환자군과 대조군으로 나누는 분류 모델을 생성하고 차이를 비교하고자 한다. 기존의 국내외에서 연구된 많은 대사성 질환과 식품 소비 특성 관련 연구는 특정 식품군이나 특정 성분의 질환 상관성 연구이며, 본 논문은 일반 식사에서 포함하는 모든 식품군을 고려한 로지스틱 회귀를 이용한 분류 모델, 의사결정나무 기반 분류 모델, XGBoost를 활용한 분류 모델을 생성하였다. 세 가지 모델 중 정확도가 높은 모델은 XGBoost 분류 모델이지만, 정확도가 0.7 미만으로 높지 않았다. 향후 연구로 환자군의 식품 소비 관찰 기간을 5년 이상으로 확대하고 섭취한 식품을 영양적 특성으로 변환한 후 대사성 질환 분류 모델 연구가 필요하다.

• Journal of Digestive Cancer Reports
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• 제7권1호
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• pp.8-12
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• 2019

Despite its declining incidence, gastric cancer is globally, still, the third most common cause of cancer-related mortality. Gastric cancer is a heterogeneous disease with diverse pathogenesis and molecular backgrounds. Therefore several systems have been proposed to aid in the classification of gastric adenocarcinoma based on the macroscopic, microscopic and anatomical features of the tumor. However, these classifications did not reflect the pathogenesis of the disease. Recently, genomic analysis has identified several subtypes of gastric adenocarcinoma and a detailed understanding of the molecular biology behind the neoplastic phenotype is possible to develop of more effective therapies. We will describe the existing various classification of gastric cancer and the recently introduced molecular biology and immunological classification.

• International Journal of Computer Science & Network Security
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• 제24권3호
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• pp.107-112
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• 2024

Nowadays the deadly virus famous as COVID-19 spread all over the world starts from the Wuhan China in 2019. This disease COVID-19 Virus effect millions of people in very short time. There are so many symptoms of COVID19 perhaps the Identification of a person infected with COVID-19 virus is really a difficult task. Moreover it's a challenging task to identify whether a person or individual have covid test positive or negative. We are developing a framework in which we used machine learning techniques..The proposed method uses DecisionTree, KNearestNeighbors, GaussianNB, LogisticRegression, BernoulliNB , RandomForest , Machine Learning methods as the classifier for diagnosis of covid ,however, 5-fold and 10-fold cross-validations were applied through the classification process. The experimental results showed that the best accuracy obtained from Decision Tree classifiers. The data preprocessing techniques have been applied for improving the classification performance. Recall, accuracy, precision, and F-score metrics were used to evaluate the classification performance. In future we will improve model accuracy more than we achieved now that is 93 percent by applying different techniques

• Journal of Information Processing Systems
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• 제17권1호
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• pp.178-190
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• 2021

Alzheimer's disease (AD) is an insidious and degenerative neurological disease. It is a new topic for AD patients to use magnetic resonance imaging (MRI) and computer technology and is gradually explored at present. Preprocessing and correlation analysis on MRI data are firstly made in this paper. Then kernel principal component analysis (KPCA) is used to extract features of brain gray matter images. Finally supervised classification schemes such as AdaBoost algorithm and support vector machine algorithm are used to classify the above features. Experimental results by means of AD program Alzheimer's Disease Neuroimaging Initiative (ADNI) database which contains brain structural MRI (sMRI) of 116 AD patients, 116 patients with mild cognitive impairment, and 117 normal controls show that the proposed method can effectively assist the diagnosis and analysis of AD. Compared with principal component analysis (PCA) method, all classification results on KPCA are improved by 2%-6% among which the best result can reach 84%. It indicates that KPCA algorithm for feature extraction is more abundant and complete than PCA.

• Asian Pacific Journal of Cancer Prevention
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• 제15권9호
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• pp.4109-4115
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• 2014

Background: Accurate assessment of disease progression requires proper understanding of natural disease process which is often hidden and unobservable. For this purpose, disease status should be clearly detected. But in most diseases it is not possible to detect such status. This study, therefore, aims to present a model which both investigates the unobservable disease process and considers the error probability in diagnosis of disease states. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at the Iran Cancer Institute from 1995 to 1999 were analyzed. Moreover, to estimate and assess the effect of demographic, diagnostic and clinical factors as well as medical and post-surgical variables on transition rates and the probability of misdiagnosis of relapse, a hidden Markov multi-state model was employed. Results: Classification errors of patients in alive state without a relapse ($e_{21}$) and with a relapse ($e_{12}$) were 0.22 (95% CI: 0.04-0.63) and 0.02 (95% CI: 0.00-0.09), respectively. Only variables of age and number of renewed treatments affected misdiagnosis of relapse. In addition, patient age and distant metastasis were among factors affecting the occurrence of relapse (state1${\rightarrow}$state2) while the number of renewed treatments and the type and extent of surgery had a significant effect on death hazard without relapse (state2${\rightarrow}$state3)and death hazard with relapse (state2${\rightarrow}$state3). Conclusions: A hidden Markov multi-state model provides the possibility of estimating classification error between different states of disease. Moreover, based on this model, factors affecting the probability of this error can be identified and researchers can be helped with understanding the mechanisms of classification error.

• 대한원격탐사학회지
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• 제35권3호
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• pp.359-373
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• 2019

소나무재선충병은 우리나라 소나무림에 심각한 위협이 되고 있는 질병이다. 그러나 일반적으로 재선충병의 고사목 관측은 현장조사를 기반으로 하고 있기 때문에 물리적, 경제적 문제가 있어 대규모 삼림을 관측하는데 어려움이 있다. 본 연구에서는 소나무재선충병이 재발한 지역에 무인 항공기를 이용하여 고해상도 영상을 획득하였다. 이후 Artificial Neural Network(ANN), Support Vector Machine(SVM) 감독분류 기법을 통해 소나무재선충병 의심목을 탐지하였고. 감독분류 결과에 대한 정확도를 산출하였다. 또한 접근성이 높은 산림에 대해 감독분류를 실시한 후 현장 조사 결과간의 비교를 통해 정확도의 신뢰성을 검증하였다.