• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.53-58
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• 2022

Pneumonia is a form of acute respiratory infection that affects the lungs. According to the World Health Organization, pneumonia is the leading cause of death for children worldwide. As a result, pneumonia was the top killer of children under the age of five years old in 2015, which is 15% of all deaths worldwide. In this paper, we used CNN model architectures to compare between the result of proposed a CNN method with VGG based model architecture. The model's performance in detecting pneumonia shows that the proposed model based on VGG can classify normal and abnormal X-rays effectively and more accurately than the proposed model used in this paper.

• 한국환경농학회:학술대회논문집
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• 2009.07a
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• pp.187-199
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• 2009

We present real.time, rapid detection of Mycoplasma pneumonia in phosphate buffered saline (PBS) inside a Y.channel polydimethylsiloxane (PDMS) microfluidic device by means of optical fiber monitoring of latex immunoagglutination. The latex immunoagglutination assay was performed with serially diluted Mycoplasma pneumonia solutions using highly carboxylated polystyrene particles of 390nm and 500nm diameter conjugated with monoclonal anti. Mycoplasma pneumonia . Proximity optical fibers were located around the viewing cell of the device, which were used to measure the increase in 45${\b{o}}$ forward light scattering of the immunoagglutinated particles. The detection limit was less than 50 $pgml^{-1}$ both for 390nm and 500nm microspheres with the detection time less than 90 seconds.

• Journal of Veterinary Clinics
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• v.36 no.4
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• pp.185-189
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• 2019

Respiratory pathogens of calves including bovine parainfluenza type 3 virus (BPI3V), bovine respiratory syncytial virus (BRSV), infectious bovine rhinotracheitis virus (IBRV) and Mycoplasma spp is well-known for winter pathogens. However, there are no studies about summer pneumonia pathogens of calves in Korea. The aim of this study was to detect respiratory pathogens from calves with summer pneumonia. Eighty calves from 5 regions were chosen and their nasal swabs were used to detect respiratory pathogens with real-time PCR. Mycoplasma spp was major primary respiratory pathogens in calves with summer pneumonia. Although, the detection rates of respiratory viruses were very low, serological assays showed that respiratory viruses exist widely in farms.

• Journal of radiological science and technology
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• v.46 no.4
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• pp.277-285
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• 2023

In this study, pneumonia identification networks with the small number of layers were constructed by using chest X-ray images. The networks had similar trainable-parameters, and the performance of the trained models was quantitatively evaluated with the modification of the network architectures. A total of 6 networks were constructed: convolutional neural network (CNN), VGGNet, GoogleNet, residual network with identity blocks, ResNet with bottleneck blocks and ResNet with identity and bottleneck blocks. Trainable parameters for the 6 networks were set in a range of 273,921-294,817 by adjusting the output channels of convolution layers. The network training was implemented with binary cross entropy (BCE) loss function, sigmoid activation function, adaptive moment estimation (Adam) optimizer and 100 epochs. The performance of the trained models was evaluated in terms of training time, accuracy, precision, recall, specificity and F1-score. The results showed that the trained models with the small number of layers precisely detect pneumonia from chest X-ray images. In particular, the overall quantitative performance of the trained models based on the ResNets was above 0.9, and the performance levels were similar or superior to those based on the CNN, VGGNet and GoogleNet. Also, the residual blocks affected the performance of the trained models based on the ResNets. Therefore, in this study, we demonstrated that the object detection networks with the small number of layers are suitable for detecting pneumonia using chest X-ray images. And, the trained models based on the ResNets can be optimized by applying appropriate residual-blocks.

• Tuberculosis and Respiratory Diseases
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• v.68 no.4
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• pp.205-211
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• 2010

Background: Procalcitonin is a well known marker in infection that plays a role in distinguishing between bacterial and viral infections in screening. The aim of the present study was to evaluate the role of procalcitonin in differentiating between 2009 H1N1 influenza pneumonia and community acquired pneumonia of bacterial origin, or mixed bacterial origin and 2009 H1N1 influenza infection. Methods: A retrospective observational study was performed over the 6-month winter period during the 2009 H1N1 influenza pandemic. Ninety-six patient-subjects were enrolled, all of whom had been diagnosed with community acquired pneumonia in emergency department during the study period. On admission, laboratory studies were performed, which included 2009 H1N1 influenza real-time polymerase chain reaction of nasal secretions and procalcitonin on serum; the laboratory values were compared between the study groups. Receiver operating characteristic curve analyses were performed on the resulting data. Results: Compared to those with bacterial or mixed infections (n=62) and bacterial pneumonia with confirmed organisms (n=30), patients with 2009 H1N1 pneumonia (n=34) were significantly more likely to have low procalcitonin levels (p=0.008, 0.001). Using cutoff of value >0.3 ng/mL, the sensitivity and specificity of procalcitonin for detection of patients with confirmed bacterial pneumonia were 76.2% and 60.6%, respectively. A significant difference in procalcitonin was found between 2009 H1N1 pneumonia and pneumonia caused by mixed influenza viral and bacterial infections (0.15 [0.05~0.84] vs. 10.3 [0.05~22.87] ng/mL, p=0.045). Conclusion: Serum procalcitonin measurement may assist in the discrimination between pneumonia of bacterial and of 2009 H1N1 influenza origin. High values of procalcitonin suggest that bacterial infection or mixed infection of bacteria and 2009 H1N1 influenza is more likely.

• Tuberculosis and Respiratory Diseases
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• v.80 no.4
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• pp.358-367
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• 2017

Background: Bacterial pneumonia occurring after respiratory viral infection is common. However, the predominant bacterial species causing pneumonia secondary to respiratory viral infections other than influenza remain unknown. The purpose of this study was to know whether the pathogens causing post-viral bacterial pneumonia vary according to the type of respiratory virus. Methods: Study subjects were 5,298 patients, who underwent multiplex real-time polymerase chain reaction for simultaneous detection of respiratory viruses, among who visited the emergency department or outpatient clinic with respiratory symptoms at Ulsan University Hospital between April 2013 and March 2016. The patients' medical records were retrospectively reviewed. Results: A total of 251 clinically significant bacteria were identified in 233 patients with post-viral bacterial pneumonia. Mycoplasma pneumoniae was the most frequent bacterium in patients aged <16 years, regardless of the preceding virus type (p=0.630). In patients aged ${\geq}16years$, the isolated bacteria varied according to the preceding virus type. The major results were as follows (p<0.001): pneumonia in patients with influenza virus (type A/B), rhinovirus, and human metapneumovirus infections was caused by similar bacteria, and the findings indicated that Staphylococcus aureus pneumonia was very common in these patients. In contrast, coronavirus, parainfluenza virus, and respiratory syncytial virus infections were associated with pneumonia caused by gram-negative bacteria. Conclusion: The pathogens causing post-viral bacterial pneumonia vary according to the type of preceding respiratory virus. This information could help in selecting empirical antibiotics in patients with post-viral pneumonia.

• Korean Journal of Veterinary Service
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• v.38 no.1
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• pp.31-36
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• 2015

Two cats were obtained from a cat kennel. Over the previous 7 days, the cats had shown cough, anorexia, depression and nasal discharge. In this study, the consensus PCR was able to detect successfully Mycoplasma species in nasal swab samples of the cats. To identify feline mycoplasma species from the lung tissue of the cats with pneumonia, Mycoplasma species-specific PCR reactions were conducted. As the results, we could identify M. felis by the positive amplified DNAs. On the other hand, we could not detect any positive reactions with the PCR reaction for M. arginini, M. canis, M. edwardii, M. cynos, M. gateae, M. maculosum, M. molared, M. opalescens, M. spumans and Mycoplasma HRC-689. In conclusion, we detected M. felis from the kenneled cats with pneumonia. We suggested that this consensus PCR would be useful and effective for monitoring Mycoplasma species in various kinds of animals including cats. The application of preceding consensus PCR before the species-specific PCRs may be the most recommended strategy for the identification of Mycoplasma spp.

• Clinical and Experimental Pediatrics
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• v.52 no.3
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• pp.315-323
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• 2009

Purpose : This study aimed to perform a systematic review of the reports on Mycoplasma pneumoniae pneumonia in the last 30 years (1980-2006) to investigate the intervals between outbreaks, change in the peak incidence age, and diagnostic methods. We also aimed to validate the proper diagnostic criteria for M. pneumoniae pneumonia. Methods : We reviewed 62 original articles on M. pneumoniae pneumonia in Korean children. We analyzed the annual or seasonal variation, study areas, patient age, journal names, and the date of each report. Further, we checked the methods and criteria used for the diagnosis of M. pneumoniae pneumonia. We also confirmed the proper mycoplasma antibody cutoff using the mycoplasma IgM titer as the gold standard. Results : In the last 30 years, epidemic outbreaks of M. pneumoniae pneumonia occurred every 3 years, except in 1993-1994 and 1996-1997. Seasonal variations were also present and were most prevalent in October and November. The number of preschool children, especially those aged 3 years or younger, with M. pneumoniae pneumonia has increased (P<0.05). The mycoplasma antibody titer of 1:640 or greater was appropriate for diagnosing M. pneumoniae pneumonia, with an acceptable sensitivity and specificity of detection. Conclusion : We analyzed the results of studies on M. pneumoniae pneumonia in Korean children during the last 30 years. Infection in younger children is increasing, and further research is required to reveal the major cause of the changing epidemics.

• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.773-780
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• 2020

With the development of the 4th industrial, research is being conducted to prevent diseases and reduce damage in various fields of science and technology such as medicine, health, and bio. As a result, artificial intelligence technology has been introduced and researched for image analysis of radiological examinations. In this paper, we will directly apply a deep learning model for classification and detection of pneumonia using chest X-ray images, and evaluate whether the deep learning model of the Inception series is a useful model for detecting pneumonia. As the experimental material, a chest X-ray image data set provided and shared free of charge by Kaggle was used, and out of the total 3,470 chest X-ray image data, it was classified into 1,870 training data sets, 1,100 validation data sets, and 500 test data sets. I did. As a result of the experiment, the result of metric evaluation of the Inception V3 deep learning model was 94.80% for accuracy, 97.24% for precision, 94.00% for recall, and 95.59 for F1 score. In addition, the accuracy of the final epoch for Inception V3 deep learning modeling was 94.91% for learning modeling and 89.68% for verification modeling for pneumonia detection and classification of chest X-ray images. For the evaluation of the loss function value, the learning modeling was 1.127% and the validation modeling was 4.603%. As a result, it was evaluated that the Inception V3 deep learning model is a very excellent deep learning model in extracting and classifying features of chest image data, and its learning state is also very good. As a result of matrix accuracy evaluation for test modeling, the accuracy of 96% for normal chest X-ray image data and 97% for pneumonia chest X-ray image data was proven. The deep learning model of the Inception series is considered to be a useful deep learning model for classification of chest diseases, and it is expected that it can also play an auxiliary role of human resources, so it is considered that it will be a solution to the problem of insufficient medical personnel. In the future, this study is expected to be presented as basic data for similar studies in the case of similar studies on the diagnosis of pneumonia using deep learning.

• Clinical and Experimental Pediatrics
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• v.57 no.4
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• pp.186-192
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• 2014

Purpose: The prevalence of macrolide-resistant Mycoplasma pneumoniae (MRMP) has increased worldwide. The aim of this study was to estimate the proportion of MRMP in a tertiary hospital in Korea, and to find potential laboratory markers that could be used to predict the efficacy of macrolides in children with MRMP pneumonia. Methods: A total of 95 patients with M. pneumoniae pneumonia were enrolled in this study. Detection of MRMP was based on the results of specific point mutations in domain V of the 23S rRNA gene. The medical records of these patients were reviewed retrospectively and the clinical course and laboratory data were compared. Results: The proportion of patients with MRMP was 51.6% and all MRMP isolates had the A2063G point mutation. The MRMP group had longer hospital stay and febrile period after initiation of macrolides. The levels of serum C-reactive protein (CRP) and interleukin-18 in nasopharyngeal aspirate were significantly higher in patients who did not respond to macrolide treatment. CRP was the only significant factor in predicting the efficacy of macrolides in patients with MRMP pneumonia. The area under the curve for CRP was 0.69 in receiver operating characteristic curve analysis, indicating reasonable discriminative power, and the optimal cutoff value was 40.7 mg/L. Conclusion: The proportion of patients with MRMP was high, suggesting that the prevalence of MRMP is rising rapidly in Korea. Serum CRP could be a useful marker for predicting the efficacy of macrolides and helping clinicians make better clinical decisions in children with MRMP pneumonia.