• Korean Journal of Digital Imaging in Medicine
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• v.11 no.1
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• pp.5-13
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• 2009

There are several reasons to take X-ray in case of inpatients. Some of them who cannot ambulate or have any risk if move are taken portable X-ray at their wards. Usually, in this case, many other people-patients unneeded X-ray test, family, hospital workers etc-are indirectly exposed to X-ray by scatter ray. For that reason I try to be aware of free space scatter dose accurately and make the point at issue of portable X-ray better in this study. kVp dose meter is used for efficiency management of portable X-ray equipment. Mobile X-ray equipment, ionization chamber, electrometer, solid water phantom are used for measuring of free space scatter dose. First of all the same surroundings condition is made as taken real portable X-ray, inquired amount of X-ray both chest AP and abdomen AP most frequently examined and measured scatter ray distribution of two tests individually changing distance. In the result of measuring horizontal distribution with condition of chest AP it is found that the mAs is decreased as law of distance reverse square but no showed mAs change according to direction. Vertical distribution showed the mAs slightly higher than horizontal distribution but it isnt found out statistical characteristic. In abdomen AP, compare with chest AP, free space scatter dose is as higher as five-hundred times and horizontal, vertical distribution are quite similar to chest AP in result. In portable X-ray test, in order to reduce the secondary exposure by free space scatter dose first, cut down unnecessary portable order the second, set up the specific area at individual ward for the test the third, when moving to a ward for the X-ray test prepare a portable shielding screen. The last, expose about 2m apart from patients if unable to do above three ways.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.219-225
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• 2022

In this study, we propose training a support vector machine (SVM) model on top of deep networks for detecting Covid-19 from chest X-ray images. We started by gathering a real chest X-ray image dataset, including positive Covid-19, normal cases, and other lung diseases not caused by Covid-19. Instead of training deep networks from scratch, we fine-tuned recent pre-trained deep network models, such as DenseNet121, MobileNet v2, Inception v3, Xception, ResNet50, VGG16, and VGG19, to classify chest X-ray images into one of three classes (Covid-19, normal, and other lung). We propose training an SVM model on top of deep networks to perform a nonlinear combination of deep network outputs, improving classification over any single deep network. The empirical test results on the real chest X-ray image dataset show that deep network models, with an exception of ResNet50 with 82.44%, provide an accuracy of at least 92% on the test set. The proposed SVM on top of the deep network achieved the highest accuracy of 96.16%.

• Journal of the Korean Society of Radiology
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• v.6 no.6
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• pp.441-445
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• 2012

X-ray Chest P-A is very important and basic diagnosis examination for pregnant women. For a pregnancy period pregnant women should be treated very carefully not to be exposed to any radiation which might cause harmful damage to women and babies as well. Lead apron is one of the effective methods to protect pregnant women from the X-ray radiation. However, it is difficult to obtain the accurate position of pregnant women during X-ray Chest P-A since conventional lead apron method forces pregnant women to hold the apron by themselves only to make pregnant women very uncomfortable and hard to maintain accurate position during radiation. As a consequence, it is common to get low quality images of X-ray Chest P-A due to the overlap of apex of lung and scapular. In order to fix this problem, we made new design lead apron that allowed pregnant women to be more comfortable to maintain accurate position during X-ray Chest P-A position. Finally, with this new design lead apron, it was possible to get the best optimized images of X-ray Chest P-A of pregnant women by minimizing overlapping apex of lung and scapular.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.2
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• pp.371-378
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• 2000

In this study, we proposed pathological pattern detection system for X-ray chest image using artificial neural network. In a physical examination, radiologists have checked on the chest image projected the view box by a magnifying glass and found out what the disease is. Here, the detection of X-ray fluoroscopy is tedious and time-consuming for human doing. Lowering of efficiency for chest diagnosis is caused by lots mistakes of radiologist because of detecting the micro pathology from the film of small size. So, we proposed the method for disease detection using artificial neural network and digital image processing on a X-ray chest image. This method composes the function of image sampling, median filter, image equalizer used neural network and pattern recognition used neural network. We confirm this method has improved the problem of a conventional method.

• Journal of Chest Surgery
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• v.31 no.7
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• pp.730-734
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• 1998

Background: It is important to know the location, number, size and shape of bullae before thoracotomy or VATS bullectomy. Chest X-ray and chest CT may be used but with some limitation. The purpose of this study was to compare the diagnostic value of thoracography with that of chest X-ray in preoperative detection of bullae. Meterial and Method: Thoracography was performed by injection of non-ionic water-soluble dye into pleural space in 22 primary spontaneous pneumothoraces, which underwent thoracotomy or VATS bullectomy. Chest X-ray and thoracography were compared through operative finding. Results: Sensitivity and accuracy of thoracography(75% and 72.7%) were higher than those of chest X-ray(30% and 36.4%). However, specificity of thoracography(50%) was lower than that of chest X-ray (100%). There were no complications during or after thoracography. Conclusion: Thoracography is a safer and more useful method for preoperative detection of bullae when compared with chest X-ray.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1059-1062
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• 1999

Recently, X-ray chest radiograph is showing a tendency to take an image of digital radiograph so as to diagnose the pathological pattern of chest in a usual. When the radiologist observes the chest image derived from digital radiograph system on the monitor. he feels difficult to find out because of the sensitivity of chest radiograph. It takes amount of time to adjust the proper image for diagnosis. Therefore, we provided the result and the method of the optimal image equalization for image enhancement.

• Journal of the Korean Society of Radiology
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• v.15 no.7
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• pp.1049-1056
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• 2021

The purpose of this study was to develop a respiratory synchronization device for X-ray (X-RSD) to increase the reproducibility of inspiration when examining the Chest X-ray of a patient who difficulty in breathing coordination. The X-RSD was self-made using an air pressure sensor and air was injected by connecting a ventilator to the mannequin for CPR. At this time, the amount of injected air was quantified using the SkillReporting device. After placing the X-RSD on the chest of the mannequin, the amount of air was tested in 6 steps from 200 to 700 cc by 100 cc increased. For the accuracy evaluation, the sensitivity of X-RSD was measured by repeating a total of 80 measurements, and the sensitivity was 100%, and very precise results were obtained. After that, the images examined while viewing the X-RSD of the chest lateral examination and the images obtained by the blind examination were compared and evaluated. The lung volume of X-RSD was larger than that of the blind test, and the deviation was smaller. Overall, the use of X-RSD can help with chest X-ray examination of patients who have difficulty in cooperating, and it is thought that it will be possible to contribute to the reduction of exposure dose by reducing the repeat rate of general X-ray examinations.

• Journal of radiological science and technology
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• v.26 no.3
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• pp.13-17
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• 2003

By converting movable indirect mass chest X-ray devices for vehicles into digital systems and upgrading it to share information with the hospital's medical image information system, excellencies have been confirmed as a result of installing and running this type of system and are listed hereinafter. 1. Upgrading analog systems, such as indirect mass chest X-ray devices dependent on printed film, to digital systems allows them to be run and managed much more efficiently, contributing to the increase in the stability and the efficiency of the system. 2. Unlike existing images, communication based on DICOM standards allow images to be compatible with the hospital's outer and inner network PACS systems, extending the scope of the radiation departments information system. 3. Assuming chest-exclusive indirect mass chest X-rays, a linked development of CAD (Computer Aided Diagnosis, Detector) becomes possible. 4. By applying wireless Internet, Web-PACS for movable indirect mass chest X-ray devices for vehicles will become possible. Research in these fields must continue and if the superior image quality and convenience of digital systems are confirmed, I believe that the conversion of systems still dependent on analog images to modernized digital systems is a must.

• Journal of the Korean Society of Radiology
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• v.14 no.1
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• pp.39-44
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• 2020

The purpose of this study was learning about chest X-ray image classification and accuracy research through Deep Learning using big data technology with Convolution Neural Network. Normal 1,583 and Pneumonia 4,289 were used in chest X-ray images. The data were classified as train (88.8%), validation (0.2%) and test (11%). Constructed as Convolution Layer, Max pooling layer size 2×2, Flatten layer, and Image Data Generator. The number of filters, filter size, drop out, epoch, batch size, and loss function values were set when the Convolution layer were 3 and 4 respectively. The test data verification results showed that the predicted accuracy was 94.67% when the number of filters was 64-128-128-128, filter size 3×3, drop out 0.25, epoch 5, batch size 15, and loss function RMSprop was 4. In this study, the classification of chest X-ray Normal and Pneumonia was predictable with high accuracy, and it is believed to be of great help not only to chest X-ray images but also to other medical images.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.313-315
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• 2021

Timely and accurate diagnosis of lung diseases using Chest X-ray images has been gained much attention from the computer vision and medical imaging communities. Although previous studies have presented the capability of deep convolutional neural networks by achieving competitive binary classification results, their models were seemingly unreliable to effectively distinguish multiple disease groups using a large number of x-ray images. In this paper, we aim to build an advanced approach, so-called Ensemble Knowledge Distillation (EKD), to significantly boost the classification accuracies, compared to traditional KD methods by distilling knowledge from a cumbersome teacher model into an ensemble of lightweight student models with parallel branches trained with ground truth labels. Therefore, learning features at different branches of the student models could enable the network to learn diverse patterns and improve the qualify of final predictions through an ensemble learning solution. Although we observed that experiments on the well-established ChestX-ray14 dataset showed the classification improvements of traditional KD compared to the base transfer learning approach, the EKD performance would be expected to potentially enhance classification accuracy and model generalization, especially in situations of the imbalanced dataset and the interdependency of 14 weakly annotated thorax diseases.