• 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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 C
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• pp.1933-1935
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• 2000

In this paper, it was tried to find out the minimum measurement range in the diagnosis of insulators using thermal image camera, for the purpose, leakage currents and thermal images were observed simultaneously for the insulators of which surface had been artificially polluted by salt fog. As a result. the surface temperature was increased with leakage currents. Also, the results of AC breakdown tests for the insulator of which temperature rise was more than 1 $^{\circ}C$ showed to be bad. Therefore, through the study on the relationship between leakage current, temperature rise and AC breakdown voltages, the diagnosis of the insulator in site would be possible using the thermal image camera.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 봄 학술논문 발표대회
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• pp.103-104
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• 2022

Cracks may occur in reinforced concrete (RC) structures due to various physical and chemical factors, and the growth of cracks causes deterioration of the structure's performance. It is important to prevent the expansion of cracks through periodic diagnosis of cracks in structures. In order to enable free crack exploration even in a narrow space, a construction robot using a Mecanum wheel that can move up, down, left and right and rotate in place was designed. High-quality crack images were periodically collected through the camera, and the image fragments stored during the exploration were combined into a single photo after the exploration was completed. The robot detected cracks with a width of 0.2 mm or more on the concrete probe surface with an accuracy of about 90% or more.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.557-560
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• 2023

In this paper, we introduce a pre-training method leveraging the capabilities of the Vision Transformer (ViT) for disease diagnosis in conventional Fundus images. Recognizing the need for effective representation learning in medical images, our method combines the Vision Transformer with a Masked Autoencoder to generate meaningful and pertinent image augmentations. During pre-training, the Masked Autoencoder produces an altered version of the original image, which serves as a positive pair. The Vision Transformer then employs contrastive learning techniques with this image pair to refine its weight parameters. Our experiments demonstrate that this dual-model approach harnesses the strengths of both the ViT and the Masked Autoencoder, resulting in robust and clinically relevant feature embeddings. Preliminary results suggest significant improvements in diagnostic accuracy, underscoring the potential of our methodology in enhancing automated disease diagnosis in fundus imaging.

• 한국인터넷방송통신학회논문지
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• 제12권4호
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• pp.181-188
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• 2012

본 논문에서는 한방 의료의 설진에서 진단 지표로 활용될 수 있는 효과적인 설태 영역 추출 방법을 제안한다. 제안한 방법은 설태의 자외선 광원에 의한 형광 반응 특성을 이용하여 기존의 설태 추출 방법의 단점으로 지적되었던 진료 환경의 제약성 및 진료 결과의 객관성 부족에 대한 문제점을 해결할 수 있다. 처리 과정으로는 자외선 광원을 사용하여 설진 영상을 획득하고, 설질(Tongue body)과 설태(Tongue coating) 영역의 색차 크기에 상응하는 히스토그램(Histogram) 상의 골-포인트(Valley-points)를 임계 처리하여 이진화(Binarization)를 수행한다. 최종적으로 설진을 위하여 한의사에게 제공되는 진단 영상은 이진 영상에 케니-에지(Canny-Edge) 알고리즘을 사용하여 설태 윤곽 정보를 추출한 후에 환자의 원 혀 영상에 부과하여 제시한다. 제안한 방법의 성능 평가를 위해서는 다양한 혀 영상을 수집하고, 한의사가 수작업으로 설정한 설태 영역을 참영상(True image)으로 하여 제안한 방법으로 추출한 설태 영역과 비교하였다. 그 결과 제안한 방법은 87.87%의 추출률을 나타냈으며, 추출된 설태 영역의 형태 유사도도 높게 나타났다.

• 한국안전학회지
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• 제19권1호
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• pp.23-30
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• 2004

Modern production systems are very complex by request of automation, and failure modes that occur in thisautomatic system are very various and complex. The efficient fault diagnosis for these complex systems is essential for productivity loss prevention and cost saving. Traditional fault diagnostic system which perforns sequential fault diagnosis can cause catastrophic failure during diagnosis when fault propagation is very fast. This paper describes the Real-time Intelligent Multiple Fault Diagnosis System (RIMFDS). RIMFDS assesses current machine condition by using sensor signals. This system deals with multiple fault diagnosis, comprising of two main parts. One is a personal computer for remote signal generation and transmission and the other is a host system for multiple fault diagnosis. The signal generator generates various faulty signals and image information and sends them to the host. The host has various modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault diagnosis and graphic representation of the results. RIMFDS diagnoses multiple faults with fast fault propagation and complex physical phenomenon. The new system based on multiprocessing diagnoses by using Hierarchical Artificial Neural Network (HANN).

• 동의생리병리학회지
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• 제23권1호
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• pp.97-103
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• 2009

The status and the property of a tongue are the important indicators to diagnose one's health like physiological and clinicopathological changes of inner organs. However, the tongue diagnosis is affected by examination circumstances like a light source, patient's posture, and doctor's condition. To develop an automatic tongue diagnosis system for an objective and standardized diagnosis, classifying tongue coating is inevitable but difficult since the features like color and texture of the tongue coatings and substance have little difference, especially in the neighborhood on the tongue surface. The proposed method has two procedures; the first is to acquire the color table to classify tongue coatings and substance by automatically separating coating regions marked by oriental medical doctors, decomposing the color components of the region into hue, saturation and brightness and obtaining the 2nd order discriminant with statistical data of hue and saturation corresponding to each kind of tongue coatings, and the other is to apply the tongue region in an input image to the color table, resulting in separating the regions of tongue coatings and classifying them automatically. As a result, kinds of tongue coatings and substance were segmented from a face image corresponding to regions marked by oriental medical doctors and the color table for classification took hue and saturation values as inputs and produced the classification of the values into white coating, yellow coating and substance in a digital tongue diagnosis system. The coating regions classified by the proposed method were almost the same to the marked regions. The exactness of classification was 83%, which is the degree of correspondence between what Oriental medical doctors diagnosed and what the proposed method classified. Since the classified regions provide effective information, the proposed method can be used to make an objective and standardized diagnosis and applied to an ubiquitous healthcare system. Therefore, the method will be able to be widely used in Oriental medicine.

• 한국한의학연구원논문집
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• 제14권3호
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• pp.97-102
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• 2008

The tongue diagnosis is a diagnostic method in the oriental medicine that uses shape, substance, coating, and movement of the tongue to determine the condition of health and disease characteristics in human. Since this information, however, could be affected by subjective sense and visual information, it is difficult to obtain the objective and reproducible results. This research aims at building a reproducible tongue diagnosis system using color chart that is attached close to the face contact region. The picture of color chart is taken simultaneously with a tongue and applied to color revision. The system, in addition, is focused on providing a clear tongue image through securing a sufficient photographing distance with a surface coating mirror. The lightning part which can suppress the reflection by sputum in maximum is implemented for the objectification and quantification of the tongue diagnosis system. The face contact region is designed for consideration of a testee's convenience. To evaluate the reproducibility of the system, the CVs (coefficient of variance, %) of $L{\ast}$, $a{\ast}$ and $b{\ast}$ of red, green and blue regions in color chart are calculated, respectively. The results of all CVs shows that the tongue diagnosis system is re liable and those consequences contribute to the objectification and quantification of the tongue diagnosis system.

• 한국멀티미디어학회논문지
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• 제22권10호
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• pp.1168-1177
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• 2019

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.

• 종양간호연구
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• 제12권2호
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• pp.156-165
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• 2012

Purpose: This study was to identify predictors of sexual function in gynecologic cancer patients. Methods: The participants were 154 patients treated at a university medical center in A city, Korea. The data collection was performed through a structured questionnaire from July to December, 2010. The instruments used in this study were Female Sexual Function Index (FSFI) perceived health status scale, Eastern Cooperative Oncology Group (ECOG) performance status, body image, and depression. Data were analyzed using descriptive statistics, Mann-Whitney test, Kruskal-Wallis test and stepwise multiple regression with the SPSS 18.0. Results: The mean score of perceived health status was 8.42 and sexual function was 8.42. The lowest score among sexual function was lubrication. The scores of sexual function was significantly different by age, job, marital status, period after diagnosis of cancer and diagnosis. There were significant correlations between sexual function, perceived health status, ECOG performance, body image and depression. In multiple regression analysis, predictors were identified as ECOG performance, age, diagnosis and period after diagnosis of cancer (Adj.$R^2$=.28). The most powerful predictor of female sexual function was ECOG performance (19.0%). Conclusion: The above findings indicate that it is necessary to develop a more effective and personalized sexual function improvement program for gynecologic cancer patient.