• Journal of the Korean Society of Radiology
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• v.16 no.1
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• pp.25-34
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• 2022

Brain computed tomography (CT) is useful for brain lesion diagnosis, such as brain hemorrhage, due to non-invasive methodology, 3-dimensional image provision, low radiation dose. However, there has been numerous misdiagnosis owing to a lack of radiologist and heavy workload. Recently, object detection technologies based on artificial intelligence have been developed in order to overcome the limitations of traditional diagnosis. In this study, the applicability of a deep learning-based YOLOv5s model was evaluated for brain hemorrhage detection using brain CT images. Also, the effect of hyperparameters in the trained YOLOv5s model was analyzed. The YOLOv5s model consisted of backbone, neck and output modules. The trained model was able to detect a region of brain hemorrhage and provide the information of the region. The YOLOv5s model was trained with various activation functions, optimizer functions, loss functions and epochs, and the performance of the trained model was evaluated in terms of brain hemorrhage detection accuracy and training time. The results showed that the trained YOLOv5s model is able to provide a bounding box for a region of brain hemorrhage and the accuracy of the corresponding box. The performance of the YOLOv5s model was improved by using the mish activation function, the stochastic gradient descent (SGD) optimizer function and the completed intersection over union (CIoU) loss function. Also, the accuracy and training time of the YOLOv5s model increased with the number of epochs. Therefore, the YOLOv5s model is suitable for brain hemorrhage detection using brain CT images, and the performance of the model can be maximized by using appropriate hyperparameters.

• Journal of Internet of Things and Convergence
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• v.4 no.2
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• pp.21-28
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• 2018

I investigated that ALK receptor tyrosine kinase (ALK) gene polymorphisms were contributed to susceptibility to ICH in Korean population. I recruited 156 ICH patients and 425 healthy controls for this study, respectively. rs1881421, rs1881420, rs3795850, and rs2246745 single nucleotide polymorphisms (SNPs) were genotyped. The genotype and allele distributions of tested four SNPs was analyzed using the SNPStats, SPSS 22.0, and the Haploview v.4.2 software. The Odd's ratios (OR), 95% confidence intervals (CI), and P values were calculated in allele and genotype models. I found that rs1881421, rs1881420, rs3795850, and rs2246745 SNPs of ALK gene (rs1881421, OR=2.02, 95% CI=1.54-2.64, p<0.001; rs1881420, OR=0.53, 95% CI=1.16-2.01, p=0.003; rs3795850, OR=1.54, 95% CI=1.17-2.02, p=0.002; rs2246745, OR=1.95, 95% CI=1.46-2.60, p<0.001 in each allele analysis). And distributions of CC, GT, and GC haplotypes between the ICH group and the control group also showed significant association with ICH (CC haplotype, p<0.001; GT haplotype, p=0.006; GC haplotype, p<0.001). These minor alleles of tested four SNPs in ALK gene were contributed to increased risk of development for ICH. Our findings suggested that the ALK gene may be a risk factor for susceptibility to ICH.The Korea Internet of Things Society.

• Investigative Magnetic Resonance Imaging
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• v.18 no.3
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• pp.258-262
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• 2014

Hypertensive encephalopathy and basal ganglia intracerebral hemorrhage (ICH) are a medical emergency caused by a sudden elevation of systemic blood pressure. Although the relationship between hypertensive encephalopathy and large ICH has not been clarified yet, Cushing reflex in acute elevations of ICP due to large ICH may induce or aggravate hypertensive encephalopathy. We report a rare case of isolated hypertensive brainstem encephalopathy combined with hypertensive ICH.

• Journal of radiological science and technology
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• v.36 no.2
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• pp.141-148
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• 2013

The CT examination sometimes fail to localize the cerebral hemorrhage part depending on the seriousness and may embarrass the pathologist if he/she is not trained enough for emergencies. Therefore, an assisting role is necessary for examination, automatic and quick detection of the cerebral hemorrhage part, and supply of the quantitative information in emergencies. the computer based automatic detection and recognition system may be of a great service to the bleeding part detection. As a result of this research, we succeeded not only in automatic detection of the cerebral hemorrhage part by grafting threshold value handling, morphological operation, and roundness calculation onto the bleeding part but also in development of the PCA based classifier to screen any wrong choice in the detection candidate group. We think if we apply the new developed system to the cerebral hemorrhage patient in his critical condition, it will be very valuable data to the medical team for operation planning.

• Journal of the Korean Society of Radiology
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• v.9 no.6
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• pp.369-374
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• 2015

In this study we proposed a texture feature analysis algorithm that distinguishes between a normal image and a diseased image using CT images of some brain hemorrhage patients, and generates both Eigen images and test images which can be applied to the proposed computer aided diagnosis system in order to perform a quantitative analysis for 6 parameters. And through the analysis, we derived and evaluated the recognition rate of CT images of brain hemorrhage. As the results of examining over 40 example CT images of brain hemorrhage, the recognition rates representing a specific texture feature-value are as follows: some appeared to be as high as 100% including average gray level, average contrast, smoothness, and Skewness while others showed a little low disease recognition rate: 95% for uniformity and 87.5% for entropy. Consequently, based on this research result, if a software that enables a computer aided diagnosis system for medical images is developed, it will lead to the availability for the automatic detection of a diseased spot in CT images of brain hemorrhage and quantitative analysis. And they can be used as computer aided diagnosis data, resulting in the increased accuracy and the shortened time in the stage of final reading.

• 건강소식
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• v.20 no.9 s.214
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• pp.14-15
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• 1996

• The Korean Chronic Disease News
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• no.22
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• pp.6-6
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• 1982

• The Korean Chronic Disease News
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• no.145
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• pp.9-9
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• 1993

• The Korean Nurse
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• v.4 no.5 s.19
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• pp.72-85
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• 1965

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.231-237
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• 2024

Brain injury causes persistent disability in survivors, and epidural hematoma(EDH) and subdural hematoma (SDH) resulting from cerebral hemorrhage can be considered one of the major clinical diseases. In this study, we attempted to automatically segment and hematomas due to cerebral hemorrhage in three dimensions based on computed tomography(CT) images. An improved GVF(gradient vector flow) algorithm was implemented for automatic segmentation of hematoma. After calculating and repeating the gradient vector from the image, automatic segmentation was performed and a 3D model was created using the segmentation coordinates. As a result of the experiment, accurate segmentation of the boundaries of the hematoma was successful. The results were found to be good even in border areas and thin hematoma areas, and the intensity, direction of spread, and area of the hematoma could be known in various directions through the 3D model. It is believed that the planar information and 3D model of the cerebral hemorrhage area developed in this study can be used as auxiliary diagnostic data for medical staff.