• 재활복지공학회논문지
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• 제10권4호
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• pp.259-265
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• 2016

본 논문에서는 폐질환 조기 검출을 위하여 Broncho vascular, Emphysema, Ground Glass Reticular, Ground Glass, Honeycomb, Normal의 6가지 폐조직에 대한 새로운 분류기법을 제안하였다. 단순 베이즈 분류기와 아다부스트 학습 기법을 도입하여 459개의 결합 히스토그램 특징인자로부터 유효한 특징인자를 선별함으로써 폐조직을 분류하였다. 다중 해상도 해석, 체적 LBP 및 CT 휘도를 기반으로 하는 결합 히스토그램 특징인자는 정확도, 민감도, 특이도 결과에서 기존의 3D AMFM보다 우수한 결과를 보였다. 제안한 특징인자와 3D AMFM 특징인자의 정확도는 각각 90.1%과 85.3%로서 제안한 특징인자의 우수한 분류 성능을 확인하였다.

• Asian Pacific Journal of Cancer Prevention
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• 제16권6호
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• pp.2107-2117
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• 2015

Thyroid cancer is the most common endocrine neoplasia. The medullary thyroid carcinoma (MTC) is one of the most aggressive forms of thyroid malignancy,accounting for up to 10% of all types of this disease. The mode of inheritance of MTC is autosomal dominantly and gain of function mutations in the RET proto-oncogene are well known to contribute to its development. MTC occurs as hereditary (25%) and sporadic (75%) forms. Hereditary MTC has syndromic (multiple endocrine neoplasia type 2A, B; MEN2A, MEN2B) and non-syndromic (Familial MTC, FMTC) types. Over the last two decades, elucidation of the genetic basis of tumorigenesis has provided useful screening tools for affected families. Advances in genetic screening of the RET have enabled early detection of hereditary MTCs and prophylactic thyroidectomy for relatives who may not show any symptom sof the disease. In this review we emphasize the main RET mutations in syndromic and non syndromic forms of MTC, and focus on the importance of RET genetic screening for early diagnosis and management of MTC patients, based on American Thyroid Association guidelines and genotype-phenotype correlation.

• 센서학회지
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• 제21권5호
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• pp.339-344
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• 2012

This article describes a novel method for the detection of amyloid-${\beta}$($A{\beta}$) peptide that utilizes a photo-sensitive field-effect transistor (p-FET). According to a recent study, $A{\beta}$ protein has been known to play a central role in the pathogenesis of Alzheimer's disease (AD). Accordingly, we investigated the variation of photo current generated from p-FET with and without intracellular magnetic beads conjugated with $A{\beta}$ peptides, which are placed on the p-FET sensing areas. The decrease of photo current was observed due to the presence of the magnetic beads on the channel region. Moreover, a similar characteristic was shown when the Raw 264 cells take in magnetic beads treated with $A{\beta}$ peptide. This means that it is possible to simply detect a certain protein using magnetic beads and a p-FET device. Therefore, in this paper, we suggest that our method could detect tiny amounts of $A{\beta}$ for early diagnosis of AD using the p-FET devices.

• 대한의용생체공학회:의공학회지
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• 제44권4호
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• pp.293-301
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• 2023

Scoliosis is a three-dimensional deformation of the spine that is a deformity induced by physical or disease-related causes as the spine is rotated abnormally. Early detection has a significant influence on the possibility of nonsurgical treatment. To train a deep learning model with preprocessed images and to evaluate the results with and without data augmentation to enable the diagnosis of scoliosis based only on a chest X-ray image. The preprocessed images in which only the spine, rib contours, and some hard tissues were left from the original chest image, were used for learning along with the original images, and three CNN(Convolutional Neural Networks) models (VGG16, ResNet152, and EfficientNet) were selected to proceed with training. The results obtained by training with the preprocessed images showed a superior accuracy to those obtained by training with the original image. When the scoliosis image was added through data augmentation, the accuracy was further improved, ultimately achieving a classification accuracy of 93.56% with the ResNet152 model using test data. Through supplementation with future research, the method proposed herein is expected to allow the early diagnosis of scoliosis as well as cost reduction by reducing the burden of additional radiographic imaging for disease detection.

• 한국멀티미디어학회논문지
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• 제23권10호
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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.

• 대한핵의학회지
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• 제37권1호
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• pp.13-23
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• 2003

Neurodegenerative disorders cause a variety of dementia including Alzheimer disease, frontotemporal dementia, dementia with Lewy bodies, corticobasal degeneration, progressive supranuclear palsy, and Huntington's disease. PET scan is useful for early detection and differential diagnosis of these dementing disorders. Also, it provides valuable information about clinico-anatomical correlation, allowing better understanding of function of brain. Here we discuss recent achievements PET studies regarding these dementing disorders. Future progress in PET technology, new tracers, and image analysis will play an important role in further clarifying the disease pathophysiology and brain functions.

• 센서학회지
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• 제32권5호
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• pp.275-279
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• 2023

Throughout the 20th century, the transition of pig farms from extensive to intensive commercial operations amplified the risk of disease transmission, particularly involving African swine fever (ASF). Real-time temperature monitoring systems have emerged as essential tools for early ASF diagnosis. In this paper, we introduce new real-time temperature monitoring ear tags (RTMEs) modeled after existing ear tag designs. Our crafted Pig-Temp platforms have three primary advantages. First, they can be effortlessly attached to pig ears, ensuring superior compatibility. Second, they enable real-time temperature detection, and the data can be displayed on a personal computer or smartphone application. Furthermore, they demonstrate excellent measurement accuracy, ranging from 98.9% to 99.8% at temperatures between 2.2 and 360℃. A linear regression approach enables fever symptoms associated with ASF to be identified within 3 min using RTMEs. The communication range extends to approximately 12 m (452 m²), enabling measurements from an estimated 75 to 2,260 pigs per gateway. These newly developed Pig-Temp platforms offer singifcant enhancement of early ASF detection.

• 대한기관식도과학회지
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• 제7권2호
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• pp.161-167
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• 2001

Background and Objectives： Total laryngectomy is often required for advanced cases. But this operation induced the many inconvenience of basic daily life. Early diagnosis of laryngeal cancer is very important to prevent from this disastrous condition. In this point of view, mass screening test for early detection of laryngeal cancer is necessary. Screening test using voice has many advantages such as simple, less interventional. Voice collection by Automatic Response System(ARS) is comfortable and easy to got acoustic sample. Thus author tried to got the acoustic parameters which can differentiate normal, benign. and malignant laryngeal diseases and also checked the availability of parameters on neural network system. Materials and Methods: Author has evaluated the voice from 17 laryngeal cancer patients and 45 benign laryngeal disease patients who visited at Department of Otolaryngology, Pusan National University Hospital from May 1998 to April 2001, and 15 normal control. Author chose the sir Parameters (Jitt. vFo, Shim, vAm, NHR, SPI) that was thought to be related with voice collected by ARS among thirty-three parameters analysed by a Multi-Dimensional Voice Program (MDVP). Two-step neural network was used for the availability of six parameters. Results: The detection rate of normal voice by ARS voice analysis is 78.5% and detection rate of abnormal voice was 97.1 o/o. Among abnormal voice, the detection rate of benign laryngeal diseases and laryngeal cancers were 82.4 o/o, 70.6% respectively. Conclusion: Author concluded that six parameters and Matlab based neural network software may be effective in development of acoustic screening system for laryngeal cancer and further study should be necessary for development of new acoustic parameters.

• Journal of Gastric Cancer
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• 제22권4호
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• pp.306-318
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• 2022

Gastric cancer (GC) is associated with high morbidity and mortality rates. Thus, early diagnosis is important to improve disease prognosis. Endoscopic assessment represents the most reliable imaging method for GC diagnosis; however, it is semi-invasive and costly and heavily depends on the skills of the endoscopist, which limit its clinical applicability. Therefore, the search for new sensitive biomarkers for the early detection of GC using noninvasive sampling collection methods has attracted much attention among scientists. Urine is considered an ideal biofluid, as it is readily accessible, less complex, and relatively stable than plasma and serum. Over the years, substantial progress has been made in screening for potential urinary biomarkers for GC. This review explores the possible applications and limitations of urinary biomarkers in GC detection and diagnosis.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• 제23권3호
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• pp.276-285
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• 2020

Purpose: Non-alcoholic fatty liver disease (NAFLD) ranges in severity from simple steatosis to steatohepatitis. Early detection of NAFLD is important for preventing the disease from progressing to become an irreversible end-stage liver disease. We developed a nomogram that allows for non-invasive screening for NAFLD in obese children. Methods: Anthropometric and laboratory data of 180 patients from our pediatric obesity clinic were collected. Diagnoses of NAFLD were based on abdominal ultrasonographic findings. The nomogram was constructed using predictors from a multivariate analysis of NAFLD risk factors. Results: The subjects were divided into non-NAFLD (n=67) and NAFLD groups (n=113). Factors, including sex, body mass index, abdominal circumference, blood pressure, insulin resistance, and levels of aspartate aminotransferase, alanine aminotransferase (ALT), γ-glutamyl transpeptidase (γGT), uric acid, triglycerides, and insulin, were significantly different between the two groups (all p<0.05) as determined using homeostatis model assessment of insulin resistance (HOMA-IR). In our multivariate logistic regression analysis, elevated serum ALT, γGT, and triglyceride levels were significantly related to NAFLD development. The nomogram was established using γGT, uric acid, triglycerides, HOMA-IR, and ALT as predictors of NAFLD probability. Conclusion: The newly developed nomogram may help predict NAFLD risk in obese children. The nomogram may also allow for early NAFLD diagnosis without the need for invasive liver biopsy or expensive liver imaging, and may also allow clinicians to intervene early to prevent the progression of NAFLD to become a more advanced liver disease.