• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.519-527
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• 2021

Alzheimer's disease is one of the challenges to tackle in the coming aging era and is attempting to diagnose and predict through various biomarkers. While the application of various deep learning-based technologies as powerful imaging technologies has recently expanded across the medical industry, empirical design is not easy because there are various deep earning neural networks architecture and categorical hyperparameters that rely on problems and data to solve. In this paper, we show the possibility of optimizing a deep learning neural network structure and hyperparameters for Alzheimer's disease classification in amyloid brain images in a representative deep earning neural networks architecture using genetic algorithms. It was observed that the optimal deep learning neural network structure and hyperparameter were chosen as the values of the experiment were converging.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.101-107
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• 2012

With the foot-and-mouth disease problems emerging as a serious social issue, this study set out to analyze the problems with the current setting of preventive zones against epidemics and find ways to minimize damage through preventive measures. For those purposes, the study analyzed the outbreaks of the foot-and-mouth disease and assumed that the disease would be transmitted via vehicles along the roads based on the network map of national roads and boundaries among administrative districts to conduct network analysis. The analysis results were then used to estimate spread time, whose results were then categorized according to lineal road distance and actual road distance. Then lineal moving speed and actual moving speed on the road were obtained according to the national roads and administrative districts to analyze the problems with the current method of setting preventive zones against the foot-and-mouth disease. As for spread speed around the areas where the foot-and-mouth disease broke out, the average lineal spread speed was 53.9km/day, and the average spread speed on the road was 71.1km/day, which indicates there are problems with the current method of setting preventive zones against epidemics.

• 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.

• The Journal of Internal Korean Medicine
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• v.38 no.6
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• pp.1007-1020
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• 2017

Objectives: This study was carried out to investigate whether a prescription's composition varies according to the disease being caused by wind, which is one of the migratory pathogenic factors. Methods: An initial database and binary matrix of Pungmun in Dongui-Bogam, including its herbs and prescription, was constructed. With this data, a network map about wind stroke, arthralgia, acute arthritis, and tetanus in Dongui-Bogam was constructed. Results: Analysis of the network map about Pungmun in Dongui-Bogam revealed that the complete prescription network has more isolated nodes than does each disease's network map. Conclusions: The composition of prescriptions in Dongui-Bogam Pungmun differ according to the disease being caused by wind.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.735-742
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• 2023

Non-alcoholic fatty liver disease is an independent risk factor for the development of cardiovascular disease, diabetes, hypertension, and kidney disease, and the clinical importance of non-alcoholic fatty liver disease has recently been increasing. In this study, we aim to extract feature values by applying GLCM, a texture analysis method, to ultrasound images of patients with non-alcoholic fatty liver disease. By applying an artificial neural network model using extracted feature values, we would like to classify the degree of fat deposition in non-alcoholic fatty liver into normal liver, mild fatty liver, moderate fatty liver, and severe fatty liver. As a result of applying the GLCM algorithm, the parameters Autocorrelation, Sum of squares, Sum average, and sum variance showed a tendency for the average value of the feature values to increase as it progressed from mild fatty liver to moderate fatty liver to severe fatty liver. The four parameters of Autocorrelation, Sum of squares, Sum average, and sum variance extracted by applying the GLCM algorithm to ultrasound images of non-alcoholic fatty liver disease were applied as inputs to the artificial neural network model. The classification accuracy was evaluated by applying the GLCM algorithm to the ultrasound images of non-alcoholic fatty liver disease and applying the extracted images to an artificial neural network, showing a high accuracy of 92.5%. Through these results, we would like to present the results of this study as basic data when conducting a texture analysis GLCM study on ultrasound images of patients with non-alcoholic fatty liver disease.

• Journal of Multimedia Information System
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• v.9 no.1
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• pp.21-32
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• 2022

Early-stage diagnosis of Alzheimer's Disease (AD) from Cognitively Normal (CN) patients is crucial because treatment at an early stage of AD can prevent further progress in the AD's severity in the future. Recently, computer-aided diagnosis using magnetic resonance image (MRI) has shown better performance in the classification of AD. However, these methods use a traditional machine learning algorithm that requires supervision and uses a combination of many complicated processes. In recent research, the performance of deep neural networks has outperformed the traditional machine learning algorithms. The ability to learn from the data and extract features on its own makes the neural networks less prone to errors. In this paper, a dense neural network is designed for binary classification of Alzheimer's disease. To create a classifier with better results, we studied result of different activation functions in the prediction. We obtained results from 5-folds validations with combinations of different activation functions and compared with each other, and the one with the best validation score is used to classify the test data. In this experiment, features used to train the model are obtained from the ADNI database after processing them using FreeSurfer software. For 5-folds validation, two groups: AD and CN are classified. The proposed DNN obtained better accuracy than the traditional machine learning algorithms and the compared previous studies for AD vs. CN, AD vs. Mild Cognitive Impairment (MCI), and MCI vs. CN classifications, respectively. This neural network is robust and better.

• IMMUNE NETWORK
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• v.11 no.5
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• pp.227-244
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• 2011

MicroRNAs (miRNAs) are small noncoding RNA molecules that negatively regulate gene expression via degradation or translational repression of their target messenger RNAs (mRNAs). Recent studies have clearly demonstrated that miRNAs play critical roles in several biologic processes, including cell cycle, differentiation, cell development, cell growth, and apoptosis and that miRNAs are highly expressed in regulatory T (Treg) cells and a wide range of miRNAs are involved in the regulation of immunity and in the prevention of autoimmunity. It has been increasingly reported that miRNAs are associated with various human diseases like autoimmune disease, skin disease, neurological disease and psychiatric disease. Recently, the identification of miRNAs in skin has added a new dimension in the regulatory network and attracted significant interest in this novel layer of gene regulation. Although miRNA research in the field of dermatology is still relatively new, miRNAs have been the subject of much dermatological interest in skin morphogenesis and in regulating angiogenesis. In addition, miRNAs are moving rapidly center stage as key regulators of neuronal development and function in addition to important contributions to neurodegenerative disorder. Moreover, there is now compelling evidence that dysregulation of miRNA networks is implicated in the development and onset of human neruodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, Tourette's syndrome, Down syndrome, depression and schizophrenia. In this review, I briefly summarize the current studies about the roles of miRNAs in various autoimmune diseases, skin diseases, psychoneurological disorders and mental stress.

• Journal of Korean Neurosurgical Society
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• v.62 no.1
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• pp.10-26
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• 2019

Magnetic resonance-guided focused ultrasound (MRgFUS) is an emerging new technology with considerable potential to treat various neurological diseases. With refinement of ultrasound transducer technology and integration with magnetic resonance imaging guidance, transcranial sonication of precise cerebral targets has become a therapeutic option. Intensity is a key determinant of ultrasound effects. High-intensity focused ultrasound can produce targeted lesions via thermal ablation of tissue. MRgFUS-mediated stereotactic ablation is non-invasive, incision-free, and confers immediate therapeutic effects. Since the US Food and Drug Administration approval of MRgFUS in 2016 for unilateral thalamotomy in medication-refractory essential tremor, studies on novel indications such as Parkinson's disease, psychiatric disease, and brain tumors are underway. MRgFUS is also used in the context of blood-brain barrier (BBB) opening at low intensities, in combination with intravenously-administered microbubbles. Preclinical studies show that MRgFUS-mediated BBB opening safely enhances the delivery of targeted chemotherapeutic agents to the brain and improves tumor control as well as survival. In addition, BBB opening has been shown to activate the innate immune system in animal models of Alzheimer's disease. Amyloid plaque clearance and promotion of neurogenesis in these studies suggest that MRgFUS-mediated BBB opening may be a new paradigm for neurodegenerative disease treatment in the future. Here, we review the current status of preclinical and clinical trials of MRgFUS-mediated thermal ablation and BBB opening, described their mechanisms of action, and discuss future prospects.

• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.28-36
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• 2017

After the genome projects of the 90s, a vast number of gene studies have been stored in online databases. By using these databases, several biological relationships can be inferred. In this study, we proposed a method to infer disease-gene relationships using title and body in biomedical text. The title was used to extract hub genes from data in the literature; whereas, the body of the literature was used to extract sub genes that are related to hub genes. Through these steps, we were able to construct a local gene-network for each report in the literature. By integrating the local gene-networks, we then constructed a global gene-network. Subsequent analyses of the global gene-network allowed inference of disease-related genes with high rank. We validated the proposed method by comparing with previous methods. The results indicated that the proposed method is a meaningful approach to infer disease-related genes.

• Safety and Health at Work
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• v.3 no.2
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• pp.92-100
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• 2012

Objectives: The French National Occupational Diseases Surveillance and Prevention Network (RNV3P) is a French network of occupational disease specialists, which collects, in standardised coded reports, all cases where a physician of any specialty, referred a patient to a university occupational disease centre, to establish the relation between the disease observed and occupational exposures, independently of statutory considerations related to compensation. The objective is to compare the relevance of disproportionality measures, widely used in pharmacovigilance, for the detection of potentially new disease ${\times}$ exposure associations in RNV3P database (by analogy with the detection of potentially new health event ${\times}$ drug associations in the spontaneous reporting databases from pharmacovigilance). Methods: 2001-2009 data from RNV3P are used (81,132 observations leading to 11,627 disease ${\times}$ exposure associations). The structure of RNV3P database is compared with the ones of pharmacovigilance databases. Seven disproportionality metrics are tested and their results, notably in terms of ranking the disease ${\times}$ exposure associations, are compared. Results: RNV3P and pharmacovigilance databases showed similar structure. Frequentist methods (proportional reporting ratio [PRR], reporting odds ratio [ROR]) and a Bayesian one (known as BCPNN for "Bayesian Confidence Propagation Neural Network") show a rather similar behaviour on our data, conversely to other methods (as Poisson). Finally the PRR method was chosen, because more complex methods did not show a greater value with the RNV3P data. Accordingly, a procedure for detecting signals with PRR method, automatic triage for exclusion of associations already known, and then investigating these signals is suggested. Conclusion: This procedure may be seen as a first step of hypothesis generation before launching epidemiological and/or experimental studies.