• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.25-34
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• 2020

In this paper, we empirically compare the effectiveness of training models to recognize beauty-related skin disease using supervised deep learning algorithms. Recently, deep learning algorithms are being actively applied for various fields such as industry, education, and medical. For instance, in the medical field, the ability to diagnose cutaneous cancer using deep learning based artificial intelligence has improved to the experts level. However, there are still insufficient cases applied to disease related to skin beauty. This study experimentally compares the effectiveness of identifying beauty-related skin disease by applying deep learning algorithms, considering CNN, ResNet, and SE-ResNet. The experimental results using these training models show that the accuracy of CNN is 71.5% on average, ResNet is 90.6% on average, and SE-ResNet is 95.3% on average. In particular, the SE-ResNet-50 model, which is a SE-ResNet algorithm with 50 hierarchical structures, showed the most effective result for identifying beauty-related skin diseases with an average accuracy of 96.2%. The purpose of this paper is to study effective training and methods of deep learning algorithms in consideration of the identification for beauty-related skin disease. Thus, it will be able to contribute to the development of services used to treat and easy the skin disease.

• Therapeutic Science for Rehabilitation
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• v.11 no.2
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• pp.63-76
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• 2022

Objective : The purpose of this study is to examine the relationship with employment of the disabled considering the severity and the type of disability. Methods : Data from the 4th data of the 2nd wave Panel Survey of Employment for the Disabled (PSED) by Korea Employment Agency for Persons with Disabilities (KEAD) were used. The odds ratio of employment in disability types according to severity of disability was calculated by logistic regression analysis. Results : When the related variables were adjusted, the employment of internal disability type was significantly lower than that of external disability type by 0.413(95% CI:0.271-0.629) times in the group with severe disability. On the other hand, in the group with less severe disability, internal disability was 0.475(95% CI:0.327-0.690) times lower than that of external disability (p=<.001). Conclusions : Employment may vary depending on the type of disability, even if the disability severity level is the same. It is necessary to prepare judgment criteria that can reduce the variation in employment by considering both the type and severity of the disability.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.595-602
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• 2022

This study tried to suggest the most efficient learning rate for accurate and efficient automatic diagnosis of medical images for chest X-ray pneumonia images using deep learning. After setting the learning rates to 0.1, 0.01, 0.001, and 0.0001 in the Inception V3 deep learning model, respectively, deep learning modeling was performed three times. And the average accuracy and loss function value of verification modeling, and the metric of test modeling were set as performance evaluation indicators, and the performance was compared and evaluated with the average value of three times of the results obtained as a result of performing deep learning modeling. As a result of performance evaluation for deep learning verification modeling performance evaluation and test modeling metric, modeling with a learning rate of 0.001 showed the highest accuracy and excellent performance. For this reason, in this paper, it is recommended to apply a learning rate of 0.001 when classifying the presence or absence of pneumonia on chest X-ray images using a deep learning model. In addition, it was judged that when deep learning modeling through the application of the learning rate presented in this paper could play an auxiliary role in the classification of the presence or absence of pneumonia on chest X-ray images. In the future, if the study of classification for diagnosis and classification of pneumonia using deep learning continues, the contents of this thesis research can be used as basic data, and furthermore, it is expected that it will be helpful in selecting an efficient learning rate in classifying medical images using artificial intelligence.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.331-337
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• 2023

In this paper, we propose a deep learning model to detect lumbar 3 (L3) CT images to determine the occurrence and degree of sarcopenia. In addition, we would like to propose an optimization technique that uses oversampling ratio and class weight as design parameters to address the problem of performance degradation due to data imbalance between L3 level and non-L3 level portions of CT data. In order to train and test the model, a total of 150 whole-body CT images of 104 prostate cancer patients and 46 bladder cancer patients who visited Gangneung Asan Medical Center were used. The deep learning model used ResNet50, and the design parameters of the optimization technique were selected as six types of model hyperparameters, data augmentation ratio, and class weight. It was confirmed that the proposed optimization-based L3 level extraction model reduced the median L3 error by about 1.0 slices compared to the control model (a model that optimized only 5 types of hyperparameters). Through the results of this study, accurate L3 slice detection was possible, and additionally, we were able to present the possibility of effectively solving the data imbalance problem through oversampling through data augmentation and class weight adjustment.

• Korean Journal of Radiology
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• v.24 no.8
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• pp.807-820
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• 2023

Objective: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. Materials and Methods: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions. CT images in groups 2-7 were converted into the target CT style (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. Results: Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2-7 improved after CT conversion (original vs. converted: 0.63 vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists' scores were significantly higher (P < 0.001) and less variable on converted CT. Conclusion: CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.

• 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.

• Management & Information Systems Review
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• v.36 no.4
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• pp.33-51
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• 2017

With the advent of 'The Forth Industrial Revolution' and the growing demand for quality of life due to economic growth, needs for the quality of medical services are increasing. Artificial intelligence has been introduced in the medical field, but it is rarely used in chronic skin diseases that directly affect the quality of life. Also, atopic dermatitis, a representative disease among chronic skin diseases, has a disadvantage in that it is difficult to make an objective diagnosis of the severity of lesions. The aim of this study is to establish an intelligent severity recognition model of atopic dermatitis for improving the quality of patient's life. For this, the following steps were performed. First, image data of patients with atopic dermatitis were collected from the Catholic University of Korea Seoul Saint Mary's Hospital. Refinement and labeling were performed on the collected image data to obtain training and verification data that suitable for the objective intelligent atopic dermatitis severity recognition model. Second, learning and verification of various CNN algorithms are performed to select an image recognition algorithm that suitable for the objective intelligent atopic dermatitis severity recognition model. Experimental results showed that 'ResNet V1 101' and 'ResNet V2 50' were measured the highest performance with Erythema and Excoriation over 90% accuracy, and 'VGG-NET' was measured 89% accuracy lower than the two lesions due to lack of training data. The proposed methodology demonstrates that the image recognition algorithm has high performance not only in the field of object recognition but also in the medical field requiring expert knowledge. In addition, this study is expected to be highly applicable in the field of atopic dermatitis due to it uses image data of actual atopic dermatitis patients.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.243-250
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• 2020

In this paper, we propose an effective software education scheme for universities. The key idea of this software education scheme is to analyze software curriculum of QS world university rankings Top 10, SW-oriented university, and regional main national university. And based on the results, we propose five improvements for the effective SW education method of universities. The first is to enhance the adaptability of the industry by developing courses based on the SW developer's job analysis in the curriculum development process. Second, it is necessary to strengthen the curriculum of the 4th industrial revolution core technologies(cloud computing, big data, virtual/augmented reality, Internet of things, etc.) and integrate them with various fields such as medical, bio, sensor, human, and cognitive science. Third, programming language education should be included in software convergence course after basic syntax education to implement projects in various fields. In addition, the curriculum for developing system programming developers and back-end developers should be strengthened rather than application program developers. Fourth, it offers opportunities to participate in industrial projects by reinforcing courses such as capstone design and comprehensive design, which enables product-based self-directed learning. Fifth, it is necessary to develop university-specific curriculum based on local industry by reinforcing internship or industry-academic program that can acquire skills in local industry field.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.789-795
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• 2020

Many deaths have been reported due to the worldwide pandemic of COVID-19. In order to prevent the further spread of COVID-19, it is necessary to quickly and accurately read images of suspected patients and take appropriate measures. To this end, this paper introduces a deep learning-based COVID-19 chest X-ray reading technique that can assist in image reading by providing medical staff whether a patient is infected. First of all, in order to learn the reading model, a sufficient dataset must be secured, but the currently provided COVID-19 open dataset does not have enough image data to ensure the accuracy of learning. Therefore, we solved the image data number imbalance problem that degrades AI learning performance by using a Stacked Generative Adversarial Network(StackGAN++). Next, the DenseNet-based classification model was trained using the augmented data set to develop the reading model. This classification model is a model for binary classification of normal chest X-ray and COVID-19 chest X-ray, and the performance of the model was evaluated using part of the actual image data as test data. Finally, the reliability of the model was secured by presenting the basis for judging the presence or absence of disease in the input image using Grad-CAM, one of the explainable artificial intelligence called XAI.

• Journal of Physiology & Pathology in Korean Medicine
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• v.33 no.5
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• pp.255-260
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• 2019

Oriental Diagnosis System(ODS) is an artificial intelligence program that utilize entered diagnosis knowledge, determine patient's disease and decide right medicine. The purpose of this study is to find a correlation between pattern Identification in Korean medicine and each sasang types(So-Yang, So-Eum and Tae-Eum) by analyzing ODS diagnosis result. Eventually our study secure availability of using ODS program at clinical training or developing diagnosis program. Subject of this study is 32 students participating in Sasang medical practice(12 subjects were So-Yang, 7 subjects were So-Eum, and 13 subjects were Tae-Eum). We analyze subject's clinical practice result reports by using ODS program and obtained result about pattern Identification. We used SPSS statistics 23 in analyzing the differences of the scores of Eight Principle Pattern Identification, Qi-Blood Pattern Identification, Bing-xie Pattern Identification, and Visceral Pattern Identification in each Sasang types (So-Yang, So-Eum, Tae-Eum). In the case of Heat-moisture, Tae-Eum showed higher score than So-Eum, but So-Yang showed no difference from the other two Sasang types(p<0.05). And in the case of Food-accumulation, Tae-Eum and So-Yang showed significantly higher score than So-Eum(p<0.05). It is hard to generalize the result because subject of this study was not enough. However, we explained correlation between pattern Identification in korean medicine and each sasang types based on quantifiable and objective evidence system. Therefore use of ODS program in student clinical practice training help to understand the relationship and correlation between different pattern Identification and will help standardization of clinical practice education.