• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4816-4834
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• 2020

This paper proposes transfer learning and fine-tuning techniques for a deep learning model to detect three distinct brain tumors from Magnetic Resonance Imaging (MRI) scans. In this work, the recent YOLOv4 model trained using a collection of 3064 T1-weighted Contrast-Enhanced (CE)-MRI scans that were pre-processed and labeled for the task. This work trained with the partial 29-layer YOLOv4-Tiny and fine-tuned to work optimally and run efficiently in most platforms with reliable performance. With the help of transfer learning, the model had initial leverage to train faster with pre-trained weights from the COCO dataset, generating a robust set of features required for brain tumor detection. The results yielded the highest mean average precision of 93.14%, a 90.34% precision, 88.58% recall, and 89.45% F1-Score outperforming other previous versions of the YOLO detection models and other studies that used bounding box detections for the same task like Faster R-CNN. As concluded, the YOLOv4-Tiny can work efficiently to detect brain tumors automatically at a rapid phase with the help of proper fine-tuning and transfer learning. This work contributes mainly to assist medical experts in the diagnostic process of brain tumors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.678-700
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• 2023

Diagnosing breast diseases using ultrasound (US) images remains challenging because it is time-consuming and requires expert radiologist knowledge. As a result, the diagnostic performance is significantly biased. To assist radiologists in this process, computer-aided diagnosis (CAD) systems have been developed and used in practice. This type of system is used not only to assist radiologists in examining breast ultrasound images (BUS) but also to ensure the effectiveness of the diagnostic process. In this study, we propose a new approach for breast lesion localization and segmentation using a multi-scale pyramid of the ultrasound image of a breast organ and a convolutional semantic segmentation network. Unlike previous studies that used only a deep detection/segmentation neural network on a single breast ultrasound image, we propose to use multiple images generated from an input image at different scales for the localization and segmentation process. By combining the localization/segmentation results obtained from the input image at different scales, the system performance was enhanced compared with that of the previous studies. The experimental results with two public datasets confirmed the effectiveness of the proposed approach by producing superior localization/segmentation results compared with those obtained in previous studies.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2005년도 춘계공동학술대회 발표논문
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• pp.475-481
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• 2005

현재 국내외에서는 유비쿼터스에 대한 연구 및 의료도메인에 대한 많은 연구가 진행되고 있다. 그러나 기존의 연구들은 전체적인 시스템에 대한 연구가 대부분이어서 실제 환경을 구축하는데 상당한 어려움이 따르고 있다. 본 연구에서는 위와 같은 문제점을 해결하기 위하여 고위험군 환자를 대상으로 다음과 같은 시나리오를 작성하였다. 시나리오는 Home -medical 서비스, Emergency call center 서비스 그리고 응급차량 서비스로 구성하였다. 본 연구에서는 위와 같은 시나리오를 기반으로 고위험군 환자의 생체 신호를 획득한 후 신경망을 이용하여 생체 신호 데이터를 학습한 후 환자의 이상 징후를 진단하는 CAD시스템의 프레임웍과 환자의 위험 수위를 단계별로 분류하는 알고리즘을 제시한다. 또한 과거의 데이터를 이용하여 미래의 환자상태를 예측하는 CAP시스템의 프레임웍을 제시하고 프레임웍에 대한 타당성을 검증하고자 한다.

• The Korean Journal of Pain
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• 제4권1호
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• pp.47-50
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• 1991

Bells palsy is a usually innocuous but psychologically distressing disease. The majority of cases are of the so-called idiopathic type, the etiology of which is unknown. This 52 year-old female patient was treated with repeated stellate ganglion bupivacaine blocks, acupuncture and transcutaneous electric nerve stimulation, with return of function. In our case studies, using thermographic images to diagnosis and to evaluate objective assessment of treatment of Bells palsy, we observed the correlation between neurologic symptoms and thermographic image.

• Radiation Oncology Journal
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• 제32권3호
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• pp.116-124
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• 2014

Purpose: To investigate the frequency and clinical significance of detected incidental lung nodules found on computed tomography (CT) simulation images for hepatocellular carcinoma (HCC) using computer-aided diagnosis (CAD) and a physician review. Materials and Methods: Sixty-seven treatment-$na{\ddot{i}}ve$ HCC patients treated with transcatheter arterial chemoembolization and radiotherapy (RT) were included for the study. Portal phase of simulation CT images was used for CAD analysis and a physician review for lung nodule detection. For automated nodule detection, a commercially available CAD system was used. To assess the performance of lung nodule detection for lung metastasis, the sensitivity, negative predictive value (NPV), and positive predictive value (PPV) were calculated. Results: Forty-six patients had incidental nodules detected by CAD with a total of 109 nodules. Only 20 (18.3%) nodules were considered to be significant nodules by a physician review. The number of significant nodules detected by both of CAD or a physician review was 24 in 9 patients. Lung metastases developed in 11 of 46 patients who had any type of nodule. The sensitivities were 58.3% and 100% based on patient number and on the number of nodules, respectively. The NPVs were 91.4% and 100%, respectively. And the PPVs were 77.8% and 91.7%, respectively. Conclusion: Incidental detection of metastatic nodules was not an uncommon event. From our study, CAD could be applied to CT simulation images allowing for an increase in detection of metastatic nodules.

• The Korean Journal of Physiology and Pharmacology
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• 제24권1호
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• pp.89-99
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• 2020

Although microscopic analysis of tissue slides has been the basis for disease diagnosis for decades, intra- and inter-observer variabilities remain issues to be resolved. The recent introduction of digital scanners has allowed for using deep learning in the analysis of tissue images because many whole slide images (WSIs) are accessible to researchers. In the present study, we investigated the possibility of a deep learning-based, fully automated, computer-aided diagnosis system with WSIs from a stomach adenocarcinoma dataset. Three different convolutional neural network architectures were tested to determine the better architecture for tissue classifier. Each network was trained to classify small tissue patches into normal or tumor. Based on the patch-level classification, tumor probability heatmaps can be overlaid on tissue images. We observed three different tissue patterns, including clear normal, clear tumor and ambiguous cases. We suggest that longer inspection time can be assigned to ambiguous cases compared to clear normal cases, increasing the accuracy and efficiency of histopathologic diagnosis by pre-evaluating the status of the WSIs. When the classifier was tested with completely different WSI dataset, the performance was not optimal because of the different tissue preparation quality. By including a small amount of data from the new dataset for training, the performance for the new dataset was much enhanced. These results indicated that WSI dataset should include tissues prepared from many different preparation conditions to construct a generalized tissue classifier. Thus, multi-national/multi-center dataset should be built for the application of deep learning in the real world medical practice.

• 대한치과보철학회지
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• 제55권4호
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• pp.410-418
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• 2017

법랑질 형성 부전증은 유전적인 결함으로 인해 구조적으로 약한 법랑질이 형성되는 질환이다. 이들 환자들은 이른 나이부터 진행되는 법랑질 마모에 의한 시린 증상과 비심미적인 치아를 주소로 치과에 내원하게 되며, 성장기 이후에는 전악 보철 수복을 통해 치아의 기능성과 심미성을 회복해 주게 된다. 법랑질 형성 부전증 환자에서 보여지는 전치부 개방 교합은, 구치의 교합면 마모 및 보상성 맹출에 의한 수직적 수복 공간 문제와 결부되어 보철 치료를 어렵게 하는 요인이 된다. 따라서 전치 길이의 결정 및 교합 고경의 거상 여부, 전방유도의 설정은 신중히 결정되어야 한다. 근래에는 Computer aided design-computer aided manufacturing (CAD/CAM) 기술을 이용하여 진단 및 최종 수복으로의 이행이 용이해 졌다. 본 증례에서는 전치부 개방교합을 가지고 있는 법랑질 형성 부전증 환자에서, CAD/CAM을 이용한 전악 수복을 시행한 후, 양호한 경과를 보이고 있기에 이를 보고하고자 한다.

• 대한치과보철학회지
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• 제57권2호
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• pp.160-170
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• 2019

상악 전치부와 같은 기능 및 심미성의 개선이 조화롭게 요구되는 부위의 치료 시에는 다른 부위보다 더 많은 지식과 기술을 필요로 한다. 특히 전방 유도(anterior guidance)를 결정하는 상악 전치부 설면 외형을 제대로 형성하지 못하면, 기능적인 불편감과 함께 전체 치열의 불안정성을 야기한다. 적절한 원리를 이용하여 전방유도를 설정한 후 임시 수복물 제작 및 조정을 통해 조화로운 전방 유도를 확보했다면 임시 수복물의 설면 외형을 최종 보철물로 정확하게 재현하는 방법에 대해 주의 깊게 고려해야 할 필요가 있다. 본 증례에서는 체계적인 진단 및 치료를 위하여 수정된 Dahl 원리(modified Dahl principle) 및 computer-aided design/computer-aided manufacturing (CAD/CAM) 시스템의 복제 기법(copy-milled)을 이용하여 적절한 전방 유도를 설정하고, 지대치의 디지털 이미지와 임시 수복물의 디지털 이미지를 중첩시켜 보다 정확하게 보철물 형태를 재현하고자 하였다. 이에 기능적, 심미적 개선을 도모하여 환자와 술자 모두에게 만족스러운 치료결과 및 예후를 얻었기에 보고하는 바이다.

• 한국컴퓨터정보학회논문지
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• 제21권1호
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• pp.9-16
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• 2016

In this paper, we present a performance evaluation agent based on fuzzy cluster analysis and validity measures. The proposed agent is consists of three modules, fuzzy cluster analyzer, performance evaluation measures, and feature ranking algorithm for feature selection step in CAD system. Feature selection is an important step commonly used to create more accurate system to help human experts. Through this agent, we get the feature ranking on the dataset of mass and calcification lesions extracted from the public real world mammogram database DDSM. Also we design a CAD system incorporating the agent and apply five different feature combinations to the system. Experimental results proposed approach has higher classification accuracy and shows the feasibility as a diagnosis supporting tool.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권1호
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• pp.68-80
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• 2013

Lung cancer is considered to be the leading cause of cancer death worldwide. A technique commonly used consists of analyzing sputum images for detecting lung cancer cells. However, the analysis of sputum is time consuming and requires highly trained personnel to avoid errors. The manual screening of sputum samples has to be improved by using image processing techniques. In this paper we present a Computer Aided Diagnosis (CAD) system for early detection and diagnosis of lung cancer based on the analysis of the sputum color image with the aim to attain a high accuracy rate and to reduce the time consumed to analyze such sputum samples. In order to form general diagnostic rules, we present a framework for segmentation and extraction of sputum cells in sputum images using respectively, a Bayesian classification method followed by region detection and feature extraction techniques to determine the shape of the nuclei inside the sputum cells. The final results will be used for a (CAD) system for early detection of lung cancer. We analyzed the performance of a Bayesian classification with respect to the color space representation and quantification. Our methods were validated via a series of experimentation conducted with a data set of 100 images. Our evaluation criteria were based on sensitivity, specificity and accuracy.