• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.4
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• pp.250-257
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• 2011

This experiment present the automatic detection algorithm of gastric cancer that take second place among all cancers. If an inflammation and a cancer are not examined carefully, early ones have difficulty in being diagnosed as illnesses than advanced ones. For diagnosis of gastric cancer, and progressing cancer in this study, present 4 algorithm. research team extracted an abnormal part in stomach through the endoscope image. At first, decide to use shading technique or not in each endoscope image for study. it make easy distinguish to whether tumor is existing or not by putting shading technique in or eliminate it by the color. Second. By passing image subjoin shading technique to erosion filter, eliminate noise and make give attention to diagnose. Third. Analyzing out a line and fillet graph from image adding surface shade and detect RED value according to degree of symptoms. Fourth. By suggesting this algorithm, that making each patient's endscope image into subdivision graph including RED graph value, afterward revers the color, revealing the position of tumor, this study desire to help to diagnosing gastric, other cancer and inflammation.

• Journal of Food Hygiene and Safety
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• v.26 no.4
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• pp.302-307
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• 2011

One of the most important objectives of post-marketing monitoring of dietary supplements is the early detection of unknown and unexpected adverse events (AEs). Several causality algorithms, such as the Naranjo scale, the RUCAM scale, and the M & V scale are available for the estimation of the likelihood of causation between a product and an AE. Based on the existing algorithms, the Korea Food & Drug Administration has developed a new algorithm tool to reflect the characteristics of dietary supplements in the causality analysis. However, additional work will be required to confirm if the newly developed algorithm tool has reasonable sensitivity and not to generate an unacceptable number of false positives signals.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.21-30
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• 2014

The use of induction motors has been recently increasing in a variety of industrial sites, and they play a significant role. This has motivated that many researchers have studied on developing fault detection and classification systems of induction motors in order to reduce economical damage caused by their faults. To early identify induction motor faults, this paper effectively estimates spectral envelopes of each induction motor fault by utilizing a linear prediction coding (LPC) analysis technique and an expectation maximization (EM) algorithm. Moreover, this paper classifies induction motor faults into their corresponding categories by calculating Mahalanobis distance using the estimated spectral envelopes and finding the minimum distance. Experimental results show that the proposed approach yields higher classification accuracies than the state-of-the-art conventional approach for both noiseless and noisy environments for identifying the induction motor faults.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.16 no.2
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• pp.70-78
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• 2016

21-hydroxylase deficiency (21-OHD), most common form of congenial adrenal hyperplasia, is categorized into classical forms, including the salt-wasting (SW) and the simple virilizing (SV) types, and nonclassical (NC) forms based on the severity of the disease. Newborn screening for 21-OHD has been performed in Korea since 2006. $17{\alpha}$-hydroxyprogesterone (17-OHP) is a marker for 21-OHD and is measured using a radioimmunoassay or a fluoroimmunoassay. Premature and low birth weight infants are likely to give false positive 17-OHP findings, therefore, cutoff values for these infants should be determined based on gestational weeks or birth weight. ACTH simulation test is helpful when the 17-OHP shows equivocal increase, and it is gold standard for diagnosis of NC type. Recently, liquid chromatography linked with tandem mass spectrometry was developed for rapid, highly specific, and sensitive analysis of multiple analytes. Molecular analysis of CYP21A2 is useful for confirming diagnosis of mild SV or NC type, predicting prognoses, and genetic counseling. In order to make newborn screening for 21-OHD more efficient, early detection of boy with SW type, early determination of girl with ambiguous genitalia, detection of NC type, and overcoming of false positive in premature and low birth weight infants should be considered. Above all, early treatment should be started when the patient is suspected as having 21- OHD clinically before confirming the diagnosis to prevent adrenal crisis. Here, author reviewed recent articles of guideline and proposed guideline for 21-OHD.

• The Journal of Bigdata
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• v.6 no.1
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• pp.37-49
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• 2021

The number of dementia patients in Korea is estimated to be over 800,000, and the severity of dementia is becoming a social problem. However, no treatment or drug has yet been developed to cure dementia worldwide. The number of dementia patients is expected to increase further due to the rapid aging of the population. Currently, early detection of dementia and delaying the course of dementia symptoms is the best alternative. This study presented a methodology for early diagnosis of dementia by measuring and analyzing amyloid plaques. This vital protein can most clearly and early diagnose dementia in the retina through AI-based image analysis. We performed binary classification and multi-classification learning based on CNN on retina data. We also developed a deep learning algorithm that can diagnose dementia early based on pre-processed retinal data. Accuracy and recall of the deep learning model were verified, and as a result of the verification, and derived results that satisfy both recall and accuracy. In the future, we plan to continue the study based on clinical data of actual dementia patients, and the results of this study are expected to solve the dementia problem.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.24 no.1
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• pp.17-25
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• 2024

Fabry disease (FD) is an X-linked lysosomal storage disorder. It is caused by mutations in the α-galactosidase A gene, which results in deficient or absent activity of α-galactosidase A (α-Gal A). This leads to a progressive accumulation of globotriaosylceramide (Gb3) in various tissues. Manifestations of Fabry disease include serious and progressive impairment of renal and cardiac function. In addition, patients experience pain, gastrointestinal disturbance, transient ischaemic attacks, and strokes. Additional effects on the skin, eyes, ears, lungs, and bones are often seen. Reduced life expectancy and deadly consequences are being caused by cardiac involvement. Chaperone therapy or enzyme replacement therapy (ERT) are two disease-specific treatments for FD. Thus, early detection of FD is critical for decreasing morbidity and mortality. Globotriaosysphingosine (lyso-Gb3) for identifying atypical FD variants and highly sensitive troponin T (hsTNT) for detecting cardiac involvement are both significant diagnostic indicators. This review aimed to offer a basic resource for the early diagnosis and update on the diagnosis of having FD. We will also provide a general diagnostic algorithm and information on ERT and its accompanying treatments.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• Journal of Biomedical Engineering Research
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• v.38 no.6
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• pp.321-329
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• 2017

This paper presents a bone metastasis Detection algorithm on abdominal computed tomography images for early detection using fully convolutional neural networks. The images were taken from patients with various cancers (such as lung cancer, breast cancer, colorectal cancer, etc), and thus the locations of those lesions were varied. To overcome the lack of data, we augmented the data by adjusting the brightness of the images or flipping the images. Before the augmentation, when 70% of the whole data were used in the pre-test, we could obtain the pixel-wise sensitivity of 18.75%, the specificity of 99.97% on the average of test dataset. With the augmentation, we could obtain the sensitivity of 30.65%, the specificity of 99.96%. The increase in sensitivity shows that the augmentation was effective. In the result obtained by using the whole data, the sensitivity of 38.62%, the specificity of 99.94% and the accuracy of 99.81% in the pixel-wise. lesion-wise sensitivity is 88.89% while the false alarm per case is 0.5. The results of this study did not reach the level that could substitute for the clinician. However, it may be helpful for radiologists when it can be used as a screening tool.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.237.1-237.1
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• 2014

Etching is one of the most important steps in semiconductor manufacturing. In etch process control a critical task is to stop the etch process when the layer to be etched has been removed. If the etch process is allowed to continue beyond this time, the material gets over-etched and the lower layer is partially removed. On the other hand if the etch process is stopped too early, part of the layer to be etched still remains, called under-etched. Endpoint detection (EPD) is used to detect the most accurate time to stop the etch process in order to avoid over or under etch. The goal of this research is to develop a hardware and software system for EPD. The hardware consists of an Optical Plasma Monitor (OPM) sensor which is used to continuously monitor the plasma optical emission intensity during the etch process. The OPM software was developed to acquire and analyze the data to perform EPD. Our EPD algorithm is based on the following theory. As the etch process starts the plasma generated in the vacuum is added with the by-products from the etch reactions on the layer being etched. As the endpoint reaches and the layer gets completely removed the plasma constituents change gradually changing the optical intensity of the plasma. Although the change in optical intensity is not apparent, the difference in the plasma constituents when the endpoint has reached leaves a unique signature in the data gathered. Though not detectable in time domain, this signature could be obscured in the frequency spectrum of the data. By filtering and analysis of the changes in the frequency spectrum before and after the endpoint we could extract this signature. In order to do that, first, the EPD algorithm converts the time series signal into frequency domain. Next the noise in the frequency spectrum is removed to look for the useful frequency constituents of the data. Once these useful frequencies have been selected, they are monitored continuously in time and using a sub-algorithm the endpoint is detected when significant changes are observed in those signals. The experiment consisted of three kinds of etch processes; ashing, SiO2 on Si etch and metal on Si etch to develop and evaluate the EPD system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.447-452
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• 2012

Among the various elements affecting a customer's evaluation of automobile quality, buzz, squeak and rattle (BSR) are considered to be major factors. In most vehicle manufacturers, the BSR problems are solved by find-fix method with the vehicle road test, mainly due to various excitation sources, complex generation mechanism and subjective response. The aim of this paper is to develop the integrated experimental method to systematically tackle the BSR problems in early stage of the vehicle development cycle by resolving these difficulties. To achieve this aim, the developed experimental method ought to include the following requirements: to find and fix the BSR problem for modules instead of a full vehicle in order to tackle the problem in the early stage of the vehicle development cycle; to develop the exciter system including the zig and road-input-signal reproducing algorithm; to automatically localize the source region of BSR; to develop sound quality index that can be used to assess the subjective responses to BSR. Also, the BSR sound quality indexes based on the Zwicker's sound quality parameters using a multiple regression analysis. The four sound metrics from Zwicker's sound quality parameter are computed for the signals recorded for eight BSR noise source regions localized by using the acoustic-field visualized results. Then, the jury test of BSR noise are performed for participants. On a basis of the computed sound metrics and jury test result, sound quality index is developed to represent the harsh of BSR noise. It is expected that the developed BSR detection system and sound quality indexes can be used to reduce the automotive interior BSR noise in terms of subjective levels as well as objective levels.