• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.677-682
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• 2012

There are lots of sampling design which is determined for sample survey in various fields. Especially, it is important problem for clinical data because basic characteristic variables by group which consist of experiment group and control group in population should be reflect to sample. Therefore, frequencies, center scales and dispersion scales of variables by group in population should be similar in sample. But usual sampling design is very complicate so it is difficult to use in practice for researcher. In this paper, we consider the sampling method using simulation. We applied the proposed method to colon cancer data from a hospital. We compare basic characteristic variables between population and sample with mean, frequency and statistic hypothesis test.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.267-272
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• 2016

Microscope cell image is an important indicator for obtaining the biological information in a bio-informatics fields. Since human observers have been examining the cell image with microscope, a lot of time and high concentration are required to analyze cell images. Furthermore, It is difficult for the human eye to quantify objectively features in cell images. In this study, we developed HCS algorithm for automatic analysis of cell image using an OpenCV library. HCS algorithm contains the cell image preprocessing, cell counting, cell cycle and mitotic index analysis algorithm. We used human cancer cell (MKN-28) obtained by the confocal laser microscope for image analysis. We compare the value of cell counting to imageJ and to a professional observer to evaluate our algorithm performance. The experimental results showed that the average accuracy of our algorithm is 99.7%.

• Korean Journal of Acupuncture
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• v.37 no.3
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• pp.159-171
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• 2020

Objectives : This study aimed to understand the characteristics of the cases covered in the case studies on traditional Korean medicine (TKM) and furthermore, to provide basic information that can lead the discussion on 'what cases are worth reporting' in future case reports. Methods : Case reports on TKM were searched using the OASIS. The searched researches were analyzed according to the type of case, including information on disease/symptoms and intervention. Results : A total of 940 researches were searched. The most frequently reported type of case study was the report on the effectiveness of intervention. Case reports, which were only two cases in the 1970s, increased rapidly in the 2000s, and in particular, 314 cases within the last five years accounted for about 33% of the total literature. As for the number of studies by disease, the cases dealing with musculoskeletal diseases such as spine, shoulder and knee joint disorders were the most prevalent. Besides, there were many case reports related to cardiovascular, gynecological, cancer, psychiatric, and dermatological diseases. In a total of 51.9% of the included case reports, a combination of two or more Korean medical treatments such as acupuncture and herbal medicine was used at once, and western treatment was used with Korean medical treatment in 28.2% of the studies. The types of Korean medical treatments were varied, such as acupuncture, moxibustion, pharmacopuncture, electroacupuncture, Chuna, acupotomy, herbal medicine, external preparation, and psychotherapy. The main purpose of the publication of the included case reports was analyzed as a report of TKM treatment for rare diseases, or the application of TKM treatment to diseases or symptoms that are "uncommon in TKM treatment" even if it is not a rare disease. Conclusions : Case reports have the strength of generating new scientific hypotheses by detecting the basic needs and novelty of medicine. The current case studies of TKM do not seem to be sufficient to highlight these strengths. It is necessary to discuss which cases are reported as cases of patients worth publishing, and based on this, it is necessary to activate case studies of TKM by utilizing diagnostic tools and science technology.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1924-1929
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• 2007

Nowadays, a lot of related data obtained from these research could be given a new present meaning to accomplish the original purpose of the whole research as a human project. The method of tumor classification based on microarray could contribute to being accurate tumor classification by finding differently expressing gene pattern statistically according to a tumor type. Therefore, the process to select a closely related informative gene with a particular tumor classification to classify tumor using present microarray technology with effect is essential. In this thesis, we used cDNA microarrays of 3840 genes obtained from neuronal differentiation experiment of cortical stem cells on white mouse with cancer, constructed accurate tumor classification model by extracting informative gene list through normalization separately and then did performance estimation by analyzing and comparing each of the experiment results. Result classifying Multi-Perceptron classifier for selected genes using Pearson correlation coefficient represented the accuracy of 95.6%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1243-1248
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• 2008

As development in technology of bioinformatics recently mates it possible to operate micro-level experiments, we can observe the expression pattern of total genome through on chip and analyze the interactions of thousands of genes at the same time. In this thesis, we used CDNA microarrays of 3840 genes obtained from neuronal differentiation experiment of cortical stem cells on white mouse with cancer. It analyzed and compared performance of each of the experiment result using existing DT, NB, SVM and multi-perceptron neural network classifier combined the similar scale combination method after constructing class classification model by extracting significant gene list with a similar scale combination method proposed in this paper through normalization. Result classifying in Multi-Perceptron neural network classifier for selected 200 genes using combination of PC(Pearson correlation coefficient) and ED(Euclidean distance coefficient) represented the accuracy of 98.84%, which show that it improve classification performance than case to experiment using other classifier.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1992-1998
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• 2007

It is important to obtain conn cytodiagnosis to classify background, cytoplasm, and nucleus from the diagnostic image. This study mose an algorithm that detects and classifies carcinoma cells of the uterine cervix in Pap smear using features of cervical cancer. It applies Median filter and Gaussian filter to get noise-removed nucleus area and also applies Kapur method in binarization of the resultant image. We apply 8-directional contour tracking algorithm and stretching technique to identify and revise clustered cells that often hinder to obtain correct analysis. The resulted nucleus area has distinguishable features such as cell size, integration rate, and directional coefficient from normal cells so that we can detect and classify carcinoma cells successfully. The experiment results show that the performance of the algorithm is competitive with human expert.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.24-34
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• 2019

In this research, a practical deep learning framework to differentiate the lesions and nodules in breast acquired with ultrasound imaging has been proposed. 7408 ultrasound breast images of 5151 patient cases were collected. All cases were biopsy proven and lesions were semi-automatically segmented. To compensate for the shift caused in the segmentation, the boundaries of each lesion were drawn using Fully Convolutional Networks(FCN) segmentation method based on the radiologist's specified point. The data set consists of 4254 benign and 3154 malignant lesions. In 7408 ultrasound breast images, the number of training images is 6579, and the number of test images is 829. The margin between the boundary of each lesion and the boundary of the image itself varied for training image augmentation. The training images were augmented by varying the margin between the boundary of each lesion and the boundary of the image itself. The images were processed through histogram equalization, image cropping, and margin augmentation. The networks trained on the data with augmentation and the data without augmentation all had AUC over 0.95. The network exhibited about 90% accuracy, 0.86 sensitivity and 0.95 specificity. Although the proposed framework still requires to point to the location of the target ROI with the help of radiologists, the result of the suggested framework showed promising results. It supports human radiologist to give successful performance and helps to create a fluent diagnostic workflow that meets the fundamental purpose of CADx.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.816-818
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• 2014

Gas target chamber has been developed for producing $^{123}I$ which is radiopharmaceuticals for diagnosis of thyroid cancer, and modeled how to occur nuclear reaction between chamber and $^{124}Xe$ with energy 30MeV inside the gas target chamber by using the MCNPX. The beam energy was lost as the beam spread when beam hit inside the gas target chamber. The cooling water was used not to change the gas target chamber as loss of energy transfer to the thermal energy. Spiral cooling line was designed for cooling the target chamber efficiently. By using the c30 cyclotron, $^{124}Xe(p,2n)$, $^{124}Xe(p,n)$, $^{124}Xe(p,pn)$ nuclear reactions were studied. In this study, we predict the production yield.

• Journal of the Korean Society for information Management
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• v.36 no.1
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• pp.117-135
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• 2019

Data journals and data papers have grown and considered an important scholarly practice in the paradigm of open science in the context of data sharing and data reuse. This study investigates a total of 713 data papers published in Scientific Data in terms of author, citation, and subject areas. The findings of the study show that the subject areas of core authors are found as the areas of Biotechnology and Physics. An average number of co-authors is 12 and the patterns of co-authorship are recognized as several closed sub-networks. In terms of citation status, the subject areas of cited publications are highly similar to the areas of data paper authors. However, the citation analysis indicates that there are considerable citations on the journals specialized on methodology. The network with authors' keywords identifies more detailed areas such as marine ecology, cancer, genome, database, and temperature. This result indicates that biology oriented-subjects are primary areas in the journal although Scientific Data is categorized in multidisciplinary science in Web of Science database.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1144-1149
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• 2020

The magnetic valve of the breast tissue expander generates imaging artifacts during MRI examination, so MRI examination is limited. To evaluate the effect of imaging artifacts on the diagnosis area for patients with breast tissue expander who need MRI examination. Imaging artifacts were measured using self-made phantoms and actual clinical conditions. Imaging artifacts were measured differently depending on the environment of 1.5 Tesla and 3.0 Tesla, and the effects of imaging artifacts were less in the C-spine and L-spine tests. If MRI due to breast cancer metastasis is absolutely necessary, head & neck examination and L-spine can be examined mainly at 1.5 Tesla, but some sequences may cause distortion due to image artifacts. In terms of safety, MRI scans of patients with breast tissue expanders can be performed conditionally at 1.5T, avoiding 3.0T.