• Clinical Endoscopy
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• v.51 no.6
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• pp.547-551
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• 2018

Unlike wired endoscopy, capsule endoscopy requires additional time for a clinical specialist to review the operation and examine the lesions. To reduce the tedious review time and increase the accuracy of medical examinations, various approaches have been reported based on artificial intelligence for computer-aided diagnosis. Recently, deep learning-based approaches have been applied to many possible areas, showing greatly improved performance, especially for image-based recognition and classification. By reviewing recent deep learning-based approaches for clinical applications, we present the current status and future direction of artificial intelligence for capsule endoscopy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.12
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• pp.1678-1684
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• 2018

Dental caries is one of the most popular oral disease. The aim of automatic dental cavity detection system is helping dentist to make accurate diagnosis. It is very important to separate cavity from the teeth in the detection system. In this paper, We compared two active contour algorithms, Snake and DRLSE(Distance Regularized Level Set Evolution). To improve performance, image is selected ROI(region of interest), then applied bilateral filter, Canny edge. In order to evaluate the algorithms, we applied to 7 tooth phantoms from incisor to molar. Each teeth contains two cavities of different shape. As a result, Snake is faster than DRLSE, but Snake has limitation to compute topology of objects. DRLSE is slower but those of performance is better.

• Annual Conference of KIPS
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• 2009.04a
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• pp.106-109
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• 2009

Recently, image matching becomes important in Computer Aided Diagnosis (CAD) due to the huge amount of medical images. Specially, texture feature is useful in medical image matching. However, texture features such as co-occurrence matrices can't describe well the spatial distribution of gray levels of the neighborhood pixels. In this paper we propose a frequency domain-based texture feature extractor that describes the local spatial distribution for medical image retrieval. This method is based on 2D Local Discrete Fourier transform of local images. The features are extracted from local Fourier histograms that generated by four Fourier images. Experimental results using 40 classes Brodatz textures and 1 class of Emphysema CT images show that the average accuracy of retrieval is about 93%.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.722-729
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• 2010

Prediction problems are widely used in medical domains. For example, computer aided diagnosis or prognosis is a key component in a CDSS (Clinical Decision Support System). SVMs with nonlinear kernels like RBF kernels, have shown superior accuracy in prediction problems. However, they are not preferred by physicians for medical prediction problems because nonlinear SVMs are difficult to visualize, thus it is hard to provide intuitive interpretation of prediction results to physicians. Nomogram was proposed to visualize SVM classification models. However, it cannot visualize nonlinear SVM models. Localized Radial Basis Function (LRBF) was proposed which shows comparable accuracy as the RBF kernel while the LRBF kernel is easier to interpret since it can be linearly decomposed. This paper presents a new tool named VRIFA, which integrates the nomogram and LRBF kernel to provide users with an interactive visualization of nonlinear SVM models, VRIFA visualizes the internal structure of nonlinear SVM models showing the effect of each feature, the magnitude of the effect, and the change at the prediction output. VRIFA also performs nomogram-based feature selection while training a model in order to remove noise or redundant features and improve the prediction accuracy. The area under the ROC curve (AUC) can be used to evaluate the prediction result when the data set is highly imbalanced. The tool can be used by biomedical researchers for computer-aided diagnosis and risk factor analysis for diseases.

• The KIPS Transactions:PartB
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• v.12B no.5 s.101
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• pp.553-560
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• 2005

Generally, it is known that the spleen accompanied by liver cirrhosis is hypertrophied or enlarged. We examined the wave pattern of the spleen by using abdominal CT images of a patient with liver cirrhosis, and found that they are different from those of a person with a normal liver In the abdominal CT image of the patient with liver cirrhosis, there is a deep wave part on the left side of the spleen. In the case of the normal liver, there are waves on the left side, but they aren't deep. Therefore, the total area of waving parts of the spleen with liver cirrhosis is found to be greater than that of the spleen with the normal liver. Moreover, when examining circularity by abstracting the waves of the spleen from the image iO liver cirrhosis, we found they are more circular than those of the spleen accompanied by a normal liver. This paper suggests an automatic method to diagnose splenic enlargement by using the wave pattern of the spleen in abdominal CT images on the basis of the two principles. It tells us that we can judge if the abdomen has a focal splenic enlargement automatically, without the manual test of the size of spleen, only with the shape of spleen.

• Journal of KIISE:Software and Applications
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• v.32 no.6
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• pp.532-541
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• 2005

We examined the wave pattern of the spleen by using abdominal CT images of a patient with liver cirrhosis, and found that they are different from those of a person with a normal liver. In the abdominal CT image of the patient with liver cirrhosis, there is a deep wave part on the left side of the spleen. In the case of the normal liver, there are waves on the left side, but they aren't deep. Therefore, the total area of waving parts of the spleen with liver cirrhosis is found to be greater than that of the spleen with the normal liver. Moreover, when examining circularity by abstracting the waves of the spleen from the image with liver cirrhosis, we found they are more circular than those of the spleen accompanied by a normal liver. This paper suggests an automatic method to diagnose liver cirrhosis by using the wave pattern of the spleen in abdominal CT images on the basis of the two principles. It tells us that we can judge if the liver has liver cirrhosis automatically, without the manual test of the ratio of caudate lobe to right lobe, only with the spleen.

• Korean Journal of Radiology
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• v.20 no.5
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• pp.749-758
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• 2019

Objective: To investigate whether a computer-aided diagnosis (CAD) system based on a deep learning framework (deep learning-based CAD) improves the diagnostic performance of radiologists in differentiating between malignant and benign masses on breast ultrasound (US). Materials and Methods: B-mode US images were prospectively obtained for 253 breast masses (173 benign, 80 malignant) in 226 consecutive patients. Breast mass US findings were retrospectively analyzed by deep learning-based CAD and four radiologists. In predicting malignancy, the CAD results were dichotomized (possibly benign vs. possibly malignant). The radiologists independently assessed Breast Imaging Reporting and Data System final assessments for two datasets (US images alone or with CAD). For each dataset, the radiologists' final assessments were classified as positive (category 4a or higher) and negative (category 3 or lower). The diagnostic performances of the radiologists for the two datasets (US alone vs. US with CAD) were compared Results: When the CAD results were added to the US images, the radiologists showed significant improvement in specificity (range of all radiologists for US alone vs. US with CAD: 72.8-92.5% vs. 82.1-93.1%; p < 0.001), accuracy (77.9-88.9% vs. 86.2-90.9%; p = 0.038), and positive predictive value (PPV) (60.2-83.3% vs. 70.4-85.2%; p = 0.001). However, there were no significant changes in sensitivity (81.3-88.8% vs. 86.3-95.0%; p = 0.120) and negative predictive value (91.4-93.5% vs. 92.9-97.3%; p = 0.259). Conclusion: Deep learning-based CAD could improve radiologists' diagnostic performance by increasing their specificity, accuracy, and PPV in differentiating between malignant and benign masses on breast US.

• Journal of the Korean Society of Radiology
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• v.18 no.5
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• pp.551-556
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• 2024

This study aims to evaluate the diagnostic concordance and accuracy by comparing thyroid lesions diagnosed with the artificial intelligence-based computer-aided diagnosis (CAD) system, S-Detect^TM, to the results of fine-needle aspiration biopsy(FNAB). A retrospective study was conducted involving 60 patients at N Hospital in Gyeongnam from May 2023 to September 2023. The study used S-Detect^TM to analyze ultrasound findings and malignancy risk of thyroid nodules and compared these findings with FNAB results to determine accuracy. The study assessed the sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of S-Detect^TM and evaluated the diagnostic concordance between the two methods using Kappa analysis. S-Detect^TM demonstrated a sensitivity of 90.5%, specificity of 83.2%, accuracy of 88.3%, PPV of 80.7%, and NPV of 92.7%. The Kappa value for diagnostic agreement between S-Detect^TM and FN AB was 0.719 (p<0.05), indicating a high level of agreement between the methods. Therefore, the CAD system S-Detect^TM proves valuable in distinguishing between malignant and benign thyroid lesions and could reduce unnecessary tissue examinations when used appropriately before thyroid fine-needle aspiration.

• Journal of Chest Surgery
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• v.25 no.10
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• pp.989-1000
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• 1992

The concepts of modern type computer are so called "General purpose, stored program and digital computer" that is proposed by Charles Babbage. ENIAC, the initial operational electronic digital computer model, was produced in 1946. During the last 50 years, an epoch-making development of the personal computer was marked. The computerization of all levels of society is going on and also computerization of the general hospital and medical college is developing. But patient data management system for clinician is not used generally. We suggest the use of computer aided data management application programs for the clinical informations of the patients of the Department of Thoracic and Cardiovascular Surgery for better management and to make best of medical informations, to co-operate with the current of this times, and to prepare against the Hospital Information Systems[HIS], actively. Also, we suggest to standardize the format and structure of database files to store the clinical data of the patients By standardization of the database files, we can integrate and relate the data of the individual department or hospital, build up the regional or national statistics of the patients easily, and promote the generation of application programs. The medical network by the communication and computer would be utilized to collect the database files. And finally, we suggest the use of code system to input and search the informations about the diagnosis and operation such as the code system of International Classfication of Disease[WHO] and the table of the classfication of operation of the Ministry of Health and Social Affairs, Korea. In this article, we tried to show the new standards, the essential items for computerization of clinical informations of the patients of the Department of Thoracic and Cardiovascular Surgery.r Surgery.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.4
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• pp.77-83
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• 2016

Recently, interests for maintenance of transmission tower are increasing to extend life of structures and reduce maintenance cost. However, existing classical diagnosis method of corrosion deteriorated degree on the transmission tower steel members, visual inspection, has a problem that error often due to difference of inspector's individual knowledge and experience. In order to solve the problem, this study carried out to develop the corrosion deterioration inspection tool for transmission tower steel members. This tool is composed of camera equipment and computer-aided diagnosis system. We standardized the photographing method by camera equipment to obtain suitable pictures for image processing. Diagnosis system was designed to evaluate automatically degree of corrosion deterioration for member of transmission tower on the basis of the RGB color image processing techniques. It is anticipated that developed the corrosion deterioration inspection tool will be very helpful in decision of optimal maintenance time for transmission tower corrosion.