• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.211-221
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• 2024

Pakistan is a top producer and exporter of high-quality rice, but traditional methods are still being used for detecting rice diseases. This research project developed an automated rice blast disease diagnosis technique based on deep learning, image processing, and transfer learning with pre-trained models such as Inception V3, VGG16, VGG19, and ResNet50. The modified connection skipping ResNet 50 had the highest accuracy of 99.16%, while the other models achieved 98.16%, 98.47%, and 98.56%, respectively. In addition, CNN and an ensemble model K-nearest neighbor were explored for disease prediction, and the study demonstrated superior performance and disease prediction using recommended web-app approaches.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.2
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• pp.102-111
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• 2007

Dementia is a major burden for many countries including South Korea, where life expectancy is continuously growing and the proportion of aged people is rapidly growing. Neurodegenerative disorders, such as, Alzheimer disease, dementia with Lewy bodies, frontotemporal dementia, Parkinson disease, progressive supranuclear palsy, corticobasal degeneration, Huntington disease, can cause dementia, and cerebrovascular disease also can cause dementia. Depression or hypothyroidism also can cause cognitive deficits, but they are reversible by management of underlying cause unlike the forementioned dementias. Therefore these are called pseudodementia. We are entering an era of dementia care that will be based upon the identification of potentially modifiable risk factors and early disease markers, and the application of new drugs postpone progression of dementias or target specific proteins that cause dementia. Efficient pharmacologic treatment of dementia needs not only to distinguish underlying causes of dementia but also to be installed as soon as possible. Therefore, differential diagnosis and early diagnosis of dementia are utmost importance. F-18 FDG PET is useful for clarifying dementing diseases and is also useful for early detection of the diseases. Purpose of this article is to review the current value of FDG PET for dementing diseases including differential diagnosis of dementia and prediction of evolving dementia.

• The Plant Pathology Journal
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• v.39 no.4
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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.

• Safety and Health at Work
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• v.11 no.3
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• pp.275-283
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• 2020

Background: In the absence of a vaccine or treatment, the most pragmatic strategies against an infectious disease pandemic are extensive early detection testing and social distancing. This study aimed to summarize public and workplace responses to Coronavirus Disease-19 (COVID-19) and show how the Korean system has operated during the COVID-19 pandemic. Method: Daily briefings from the Korean Center for Disease Control and the Central Disaster Management Headquarters were assembled from January 20 to May 15, 2020. Results: By May 15, 2020, 11,018 COVID-19 cases were identified, of which 15.7% occurred in workplaces such as health-care facilities, call centers, sports clubs, coin karaoke, and nightlife destinations. When the first confirmed case was diagnosed, the Korean Center for Disease Control and Central Disaster Management Headquarters responded quickly, emphasizing early detection with numerous tests and a social distancing policy. This slowed the spread of infection without intensive containment, shut down, or mitigation interventions. After entering the public health blue alert level, a business continuity plan was distributed. After entering the orange level, the Ministry of Employment and Labor developed workplace guidelines for COVID-19 consisting of social distancing, flexible working schedules, early identification of workers with suspected infections, and disinfection of workplaces. Owing to the intensive workplace social distancing policy, workplaces remained safe with only small sporadic group infections. Conclusion: The workplace social distancing policy with timely implementation of specific guidelines was a key to preventing a large outbreak of COVID-19 in Korean workplaces. However, sporadic incidents of COVID-19 are still ongoing, and risk assessment in vulnerable workplaces should be continued.

• Dementia and Neurocognitive Disorders
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• v.21 no.4
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• pp.126-137
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• 2022

Background and Purpose: Early detection of subjective cognitive decline (SCD) due to Alzheimer's disease (AD) is important for clinical research and effective prevention and management. This study examined if quantitative electroencephalography (qEEG) could be used for early detection of AD in SCD. Methods: Participants with SCD from 6 dementia clinics in Korea were enrolled. ¹⁸F-florbetaben brain amyloid positron emission tomography (PET) was conducted for all the participants. qEEG was performed to measure power spectrum and source cortical activity. Results: The present study included 95 participants aged over 65 years, including 26 amyloid PET (+) and 69 amyloid PET (-). In participants with amyloid PET (+), relative power at delta band was higher in frontal (p=0.025), parietal (p=0.005), and occipital (p=0.022) areas even after adjusting for age, sex, and education. Source activities of alpha 1 band were significantly decreased in the bilateral fusiform and inferior temporal areas, whereas those of delta band were increased in the bilateral cuneus, pericalcarine, lingual, lateral occipital, precuneus, posterior cingulate, and isthmus areas. There were increased connections between bilateral precuneus areas but decreased connections between left rostral middle frontal area and bilateral frontal poles at delta band in participants with amyloid PET (+) showed. At alpha 1 band, there were decreased connections between bilateral entorhinal areas after adjusting for covariates. Conclusions: SCD participants with amyloid PET (+) showed increased delta and decreased alpha 1 activity. qEEG is a potential means for predicting amyloid pathology in SCD. Further longitudinal studies are needed to confirm these findings.

• Korean Journal of Biological Psychiatry
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• v.20 no.4
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• pp.119-128
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• 2013

Objectives Memory impairment is a very important mental health issue for elderly and adults. Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD). Early detection of the prodromal stage of patients with AD is an important topic of interest for both mental health clinicians and policy makers. Methods Electroencephalograpgy (EEG) has been used as a possible biological marker for patients with MCI, and AD. In this review, we will summarize the clinical implications of EEG and ERP as a biological marker for AD and MCI. Results EEG power density, functional coupling, spectral coherence, synchronization, and connectivity were analyzed and proved their clinical efficacy in patients with the prodromal stage of AD. Serial studies on late event-related potentials (ERPs) were also conducted in MCI patients as well as healthy elders. Even though these EEG and ERP studies have some limitations for their design and method, their clinical implications are increasing rapidly. Conclusion EEG and ERP can be used as biological markers of AD and MCI. Also they can be used as useful tools for early detection of AD and MCI patients. They are useful and sensitive research tools for AD and MCI patients. However, some problems remain to be solved until they can be practical measures in clinical setting.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.51-62
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• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.

• The Journal of the Korean dental association
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• v.49 no.3
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• pp.136-145
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• 2011

Oral cavity cancer accounts for approximately 3-4% of all malignancies and is a significant worldwide health problem. The Korea Central Cancer Registry estimates that there will be approximately 1500 new cases of oral cancer in Korea. Oral cancer occurs most commonly in middle-aged and elderly individuals. The majority of oral malignancies occur as squamous cell carcinomas and despite remarkable advances in treatment modalities, the 5-year survival rate has not significantly improved over the past several decades, hovering at about 50% to 60%. The unfavorable 5-year survival rate may be attributable to several factors. First, oral cancer is often diagnosed at a late stage, with late stage 5-year survival rates as low as 22%. Additionally, the development of secondary primary tumors in patients with early stage disease has a major impact on survival. The early detection of oral cancer and premalignant lesions offers the promise to cure chance of oral cancer. The major diagnostics moddalities for oral cancer include oral cavity examination, supravital staining, oral cytology, and optical detection systems. But the clinical finding of oral mucosa is the most important key to confirm the oral cancer until now. The traditional clinical examination of oral cavity can be performed quickly, is without additional diagnostic expense to patients, and may be performed by health care professionals. Therefore, clinicians must be well-acquainted with clinical characteristics of oral cancer and practice routine screening for oral cancer in dental clinic to decrease the morbidity and mortality of disease.

• The Korean Journal of Physiology and Pharmacology
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• v.18 no.6
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• pp.447-456
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• 2014

Until now, a disease-modifying therapy (DMT) that has an ability to slow or arrest Alzheimer's disease (AD) progression has not been developed, and all clinical trials involving AD patients enrolled by clinical assessment alone also have not been successful. Given the growing consensus that the DMT is likely to require treatment initiation well before full-blown dementia emerges, the early detection of AD will provide opportunities to successfully identify new drugs that slow the course of AD pathology. Recent advances in early detection of AD and prediction of progression of the disease using various biomarkers, including cerebrospinal fluid (CSF) $A{\beta}_{1-42}$, total tau and p-tau181 levels, and imagining biomarkers, are now being actively integrated into the designs of AD clinical trials. In terms of therapeutic mechanisms, monitoring these markers may be helpful for go/no-go decision making as well as surrogate markers for disease severity or progression. Furthermore, CSF biomarkers can be used as a tool to enrich patients for clinical trials with prospect of increasing statistical power and reducing costs in drug development. However, the standardization of technical aspects of analysis of these biomarkers is an essential prerequisite to the clinical uses. To accomplish this, global efforts are underway to standardize CSF biomarker measurements and a quality control program supported by the Alzheimer's Association. The current review summarizes therapeutic targets of developing drugs in AD pathophysiology, and provides the most recent advances in the clinical utility of CSF biomarkers and the integration of CSF biomarkers in current clinical trials.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.4 no.1
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• pp.13-17
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• 2004

Hereditary tyrosinemia type I (fiunarylacetoacetate hydrolase deficiency) is an autosomal recessive inborn error of tyrosine metabolism that results in liver failure in infancy or chronic liver disease with cirrhosis, frequently complicated by hepatocellular carcinoma in childhood or early adolescence. Early detection of this condition is very important to early intervention for better prognosis of patients. Neonatal screening test using tandem mass spectrometry (MS-MS) is performed, and this method facilitates detection of the inborn error of tyrosine. For early treatment of tyrosinemia type I, phenylalanine and tyrosine restricted diet and NTBC (2-nitro-4-trifluoromethylbenzoyl-1,3-cyclohexanedione) for inhibition of succinylacetone production are recommended. We studied a 10-month-old Korean boy with tyrosinemia type I whose condition was not discovered earlier through conventional neonatal screening testing available in Korea. The patient presented hyperbilirubinemia, liver failure, bleeding tendency, colicky pain and skin melanin pigmentation in neonatal period. MS-MS made it possible to detect tyrosinemia type I and allowed immediate treatment of the patient. This was the first successful NTBC trial on tyrosinemia type I patient in Korea.