• Parasites, Hosts and Diseases
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• v.21 no.2
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• pp.265-269
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• 1983

A comparison was made of a new serological method, thin layer immunoassay (TIA), and an established method, enzyme-linked immunosorbent assay (ELISA), in the detection and quantiacation of antibodies in clonorchiasis. Saline extract of Iyophilized Clonorchis sinensis adult worm was used as antigen, and TIA by the method of Elwing et at. (1976) and ELISA by Voller et at. (1974) were performed. Using sera from known clonorchiasis cases,100% of the sera tested were Positive by TIA and 88.35 by ELISA. TIA produced false positive results in 14 out of 36 cases, which were 10 amoebiasis cases, 16 paragonimiasis cases and 10 healthy controls. ELISA. however, produced a small number of false positives, 7 out of 55 cases. There was correlation between Immunoglobulin G level in sera and ELISA value (correlation coefficient, 0.69), whereas no correlation between Immunoglobulin G level and TIA result. The Performance of TIA and ELISA was not correlated in the results using homologous antigen.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.1
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• pp.423-426
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• 2014

Aim: To evaluate the agreement between colposcopic diagnosis and cervical pathology a retrospective chart review was performed. Materials and Methods: This study included 437 patients who underwent colposcopy and cervical biopsy or conization at Siriraj Hospital from October 2010 - December 2012. The patient clinical characteristics, cervical cytology results, colposcopic diagnoses, cervical pathology results were recorded and correlations between variables were analyzed. Results: Agreement of colposcopic diagnosis and cervical pathology was matched in 253 patients (57.9%). The strength of agreement with weighted Kappa statistic was 0.494 (p<0.001). Colposcopic diagnoses more often overestimated (31.1%) than underestimated (11%) the cervical pathology. Agreement of colposcopic diagnosis and cervical pathology within 1 grade was found in 411 patients (94.1%). Positive predictive value (PPV) of high grade colposcopy or more was 75.5%, whereas the negative predictive value (NPV) of insignificant and low grade colposcopy was 83.8%. False positives of high grade colposcopy or more were 21%. False negatives of insignificant or low grade colposcopy were 19.1%. Conclusions: Strength of agreement between colposcopic diagnosis and cervical pathology was found to be only moderate. A biopsy at colposcopy should be performed at a gold standard level to detect high grade lesions.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.73-81
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• 2011

In this paper, an improved block-based background modeling technique using adaptive parameter estimation that judiciously adjusts the number of model histograms at each frame sequence is proposed. The conventional block-based background modeling method has a fixed number of background model histograms, resulting to false negatives when the image sequence has either rapid illumination changes or swiftly moving objects, and to false positives with motionless objects. In addition, the number of optimal model histogram that changes each type of input image must have found manually. We demonstrate the proposed method is promising through representative performance evaluations including the background modeling in an elevator environment that may have situations with rapid illumination changes, moving objects, and motionless objects.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.511-520
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• 2016

Automatic License Plate Detection (ALPD) is a key technology for a efficient traffic control. It is used to improve work efficiency in many applications such as toll payment systems and parking and traffic management. Until recently, the hand-crafted features made for image processing are used to detect license plates in most studies. It has the advantage in speed. but can degrade the detection rate with respect to various environmental changes. In this paper, we propose a way to utilize a Faster Region based Convolutional Neural Networks (Faster R-CNN) and a Conventional Convolutional Neural Networks (CNN), which improves the computational speed and is robust against changed environments. The module based on Faster R-CNN is used to detect license plate candidate regions from images and is followed by the module based on CNN to remove False Positives from the candidates. As a result, we achieved a detection rate of 99.94% from images captured under various environments. In addition, the average operating speed is 80ms/image. We implemented a fast and robust Real-Time License Plate Detection System.

• Imaging Science in Dentistry
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• v.53 no.4
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• pp.271-281
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• 2023

Purpose: The objective of this scoping review was to investigate the applicability and performance of various convolutional neural network (CNN) models in tooth numbering on panoramic radiographs, achieved through classification, detection, and segmentation tasks. Materials and Methods: An online search was performed of the PubMed, Science Direct, and Scopus databases. Based on the selection process, 12 studies were included in this review. Results: Eleven studies utilized a CNN model for detection tasks, 5 for classification tasks, and 3 for segmentation tasks in the context of tooth numbering on panoramic radiographs. Most of these studies revealed high performance of various CNN models in automating tooth numbering. However, several studies also highlighted limitations of CNNs, such as the presence of false positives and false negatives in identifying decayed teeth, teeth with crown prosthetics, teeth adjacent to edentulous areas, dental implants, root remnants, wisdom teeth, and root canal-treated teeth. These limitations can be overcome by ensuring both the quality and quantity of datasets, as well as optimizing the CNN architecture. Conclusion: CNNs have demonstrated high performance in automated tooth numbering on panoramic radiographs. Future development of CNN-based models for this purpose should also consider different stages of dentition, such as the primary and mixed dentition stages, as well as the presence of various tooth conditions. Ultimately, an optimized CNN architecture can serve as the foundation for an automated tooth numbering system and for further artificial intelligence research on panoramic radiographs for a variety of purposes.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.267-275
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• 2023

This paper presents the development of specialized software for annotating volume-of-interest on ¹⁸F-FDG PET/CT images with the goal of facilitating the studies and diagnosis of head and neck cancer (HNC). To achieve an efficient annotation process, we employed the SE-Norm-Residual Layer-based U-Net model. This model exhibited outstanding proficiency to segment cancerous regions within ¹⁸F-FDG PET/CT scans of HNC cases. Manual annotation function was also integrated, allowing researchers and clinicians to validate and refine annotations based on dataset characteristics. Workspace has a display with fusion of both PET and CT images, providing enhance user convenience through simultaneous visualization. The performance of deeplearning model was validated using a Hecktor 2021 dataset, and subsequently developed semi-automatic annotation functionalities. We began by performing image preprocessing including resampling, normalization, and co-registration, followed by an evaluation of the deep learning model performance. This model was integrated into the software, serving as an initial automatic segmentation step. Users can manually refine pre-segmented regions to correct false positives and false negatives. Annotation images are subsequently saved along with their corresponding ¹⁸F-FDG PET/CT fusion images, enabling their application across various domains. In this study, we developed a semi-automatic annotation software designed for efficiently generating annotated lesion images, with applications in HNC research and diagnosis. The findings indicated that this software surpasses conventional tools, particularly in the context of HNC-specific annotation with ¹⁸F-FDG PET/CT data. Consequently, developed software offers a robust solution for producing annotated datasets, driving advances in the studies and diagnosis of HNC.

• Journal of the Korean Society of Radiology
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• v.82 no.1
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• pp.29-48
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• 2021

Diffusion-weighted magnetic resonance imaging (DW MRI) is a fast unenhanced technique that shows promise as a stand-alone modality for cancer screening and characterization. Currently, DW MRI may have lower sensitivity than that of dynamic contrast-enhanced MRI as a standalone modality for breast cancer detection but superior to that of mammography, which may provide a useful alternative for supplemental screening. Standardized acquisition and interpretation of DW MRI can improve the image quality and reduce the variability of the results. Furthermore, high-resolution DW MRI, with advanced techniques and postprocessing, will facilitate better detection and characterization of subcentimeter cancers and reduce false-negatives and false-positives. Future results from ongoing prospective multicenter clinical trials using standardized and optimized protocols will facilitate the use of DW MRI as a stand-alone modality.

• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.157-167
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• 2009

Recently, the leakage of confidential information and personal information is taking place on the Internet more frequently than ever before. Most of such online security incidents are caused by attacks on vulnerabilities in web applications developed carelessly. It is impossible to detect an attack on a web application with existing firewalls and intrusion detection systems. Besides, the signature-based detection has a limited capability in detecting new threats. Therefore, many researches concerning the method to detect attacks on web applications are employing anomaly-based detection methods that use the web traffic analysis. Much research about anomaly-based detection through the normal web traffic analysis focus on three problems - the method to accurately analyze given web traffic, system performance needed for inspecting application payload of the packet required to detect attack on application layer and the maintenance and costs of lots of network security devices newly installed. The UTM(Unified Threat Management) system, a suggested solution for the problem, had a goal of resolving all of security problems at a time, but is not being widely used due to its low efficiency and high costs. Besides, the web filter that performs one of the functions of the UTM system, can not adequately detect a variety of recent sophisticated attacks on web applications. In order to resolve such problems, studies are being carried out on the web application firewall to introduce a new network security system. As such studies focus on speeding up packet processing by depending on high-priced hardware, the costs to deploy a web application firewall are rising. In addition, the current anomaly-based detection technologies that do not take into account the characteristics of the web application is causing lots of false positives and false negatives. In order to reduce false positives and false negatives, this study suggested a realtime anomaly detection method based on the analysis of the length of parameter value contained in the web client's request. In addition, it designed and suggested a WAF(Web Application Firewall) that can be applied to a low-priced system or legacy system to process application data without the help of an exclusive hardware. Furthermore, it suggested a method to resolve sluggish performance attributed to copying packets into application area for application data processing, Consequently, this study provide to deploy an effective web application firewall at a low cost at the moment when the deployment of an additional security system was considered burdened due to lots of network security systems currently used.

• ETRI Journal
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• v.31 no.5
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• pp.545-553
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• 2009

String matching is a fundamental element of an important category of modern packet processing applications which involve scanning the content flowing through a network for thousands of strings at the line rate. To keep pace with high network speeds, specialized hardware-based solutions are needed which should be efficient enough to maintain scalability in terms of speed and the number of strings. In this paper, a novel architecture based upon a recently proposed data structure called the Bloomier filter is proposed which can successfully support scalability. The Bloomier filter is a compact data structure for encoding arbitrary functions, and it supports approximate evaluation queries. By eliminating the Bloomier filter's false positives in a space efficient way, a simple yet powerful exact string matching architecture is proposed that can handle several thousand strings at high rates and is amenable to on-chip realization. The proposed scheme is implemented in reconfigurable hardware and we compare it with existing solutions. The results show that the proposed approach achieves better performance compared to other existing architectures measured in terms of throughput per logic cells per character as a metric.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.1-7
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• 2016

As new information and communication technologies evolve, security threats are also becoming increasingly intelligent and advanced. In this paper, we analyze the time series data continuously entered through a series of periods from the network device or lightweight IoT (Internet of Things) devices by using the statistical technique and propose a system to detect abnormal behaviors of the device or abnormality based on the analysis results. The proposed system performs the first level abnormal detection by using previously entered data set, thereafter performs the second level anomaly detection according to the trust bound configured by using stored time series data based on time attribute or group attribute. Multi-level analysis is able to improve reliability and to reduce false positives as well through a variety of decision data set.