• Asian Pacific Journal of Cancer Prevention
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• v.16 no.9
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• pp.3867-3870
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• 2015

Background: To explore whether combined detection of serum tumor markers (CEA, CA72-4, CA19-9 and TSGF) improve the sensitivity and accuracy in the diagnosis of gastric cancer (GC). Materials and Methods: An automatic chemiluminescence immune analyzer with matched kits were used to determine the levels of serum CEA, CA72-4, CA19-9 and TSGF in 45 patients with gastric cancer (GC group), 40 patients with gastric benign diseases (GBD group) hospitalized in the same period and 30 healthy people undergoing a physical examination. The values of those 4 tumor markers in the diagnosis of gastric cancer was analyzed. Results: The levels of serum CEA, CA72-4, CA19-9 and TSGF of the GC group were higher than those of the GBD group and healthy examined people and the differences were significant (P<0.001). The area under receiver operating characteristic (ROC) curves for single detection of CEA, CA72-4, CA19-9 and TSGF in the diagnosis of GC was 0.833, 0.805, 0.810 and 0.839, respectively. The optimal cutoff values for these 4 indices were 2.36 ng/mL, 3.06 U/mL, 5.72 U/mL and 60.7 U/mL, respectively. With combined detection of tumor markers, the diagnostic power of those 4 indices was best, with an area under the ROC curve of 0.913 (95%CI 0.866~0.985), a sensitivity of 88.9% and a diagnostic accuracy of 90.4%. Conclusions: Combined detection of serum CEA, CA72-4, CA19-9 and TSGF increases the sensitivity and accuracy in diagnosis of GC, so it can be regarded as the important means for early diagnosis.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.967-970
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• 2020

In a previous work we have done, we presented a monitoring system to automatically detect some dogs' behaviors from videos. However, the input video data used by that system was pre-trimmed to ensure it contained a dog only. In a real-life situation, the monitoring system would continuously receive video data, including frames that are empty and ones that contain people. In this paper, we propose a YOLOv4-based system for automatic object detection and trimming of dog videos. Sequences of frames trimmed from the video data received from the camera are analyzed to detect dogs and people frame by frame using a YOLOv4 model, and then records of the occurrences of dogs and people are generated. The records of each sequence are then analyzed through a rule-based decision tree to classify the sequence, forward it if it contains a dog only or ignore it otherwise. The results of the experiments on long untrimmed videos show that our proposed method manages an excellent detection performance reaching 0.97 in average of precision, recall and f-1 score at a detection rate of approximately 30 fps, guaranteeing with that real-time processing.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.163-169
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• 2008

In this paper, we propose a real-time people tracking system with multiple CCD cameras for security inside the building. The camera is mounted from the ceiling of the laboratory so that the image data of the passing people are fully overlapped. The implemented system recognizes people movement along various directions. To track people even when their images are partially overlapped, the proposed system estimates and tracks a bounding box enclosing each person in the tracking region. The approximated convex hull of each individual in the tracking area is obtained to provide more accurate tracking information. To achieve this goal, we propose a method for 3D walking human tracking based on the IMPRESARIO framework incorporating cascaded classifiers into hypothesis evaluation. The efficiency of adaptive selection of cascaded classifiers have been also presented. We have shown the improvement of reliability for likelihood calculation by using cascaded classifiers. Experimental results show that the proposed method can smoothly and effectively detect and track walking humans through environments such as dense forests.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.9-15
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• 2021

It is a challenge for visually impaired people to access unfamiliar environments independently, hence the quality of life is reduced, and safety of life is compromised. An accurate and reliable door detection system comprising of way finding and indoor navigation can be beneficial for a large number of autonomous and mobile applications for visually impaired people. This paper illustrates an image-based door detection scheme for visually impaired people using stable features (edges and corners) including color averaging and image resizing. Simulation results show that the proposed scheme shows a significant improvement when compared with existing scheme.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.112-119
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• 2021

Social media has become a global means of communication in people's lives. Most people are using Twitter for communication purposes and its inappropriate use, which has negative effects on people's lives. One of the widely common misuses of Twitter is cyberbullying. As the resources of dialectal Arabic are rare, so for cyberbullying most people are using dialectal Arabic. For this reason, the ultimate goal of this study is to detect and classify cyberbullying on Twitter in the Arabic context in Saudi Arabia. To help in the detection and classification of tweets, Pointwise Mutual Information (PMI) to generate a lexicon, and Support Vector Machine (SVM) algorithms are used. The evaluation is performed on both methods in terms of the F1-score. However, the F1-score after applying the PMI is 50%, while after the SVM application on the resampling data it is 82%. The analysis of the results shows that the SVM algorithm outperforms better.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.610-630
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• 2020

A large number of people suffered from traffic accidents each year, so people pay more attention to traffic safety. However, the traditional methods use laser sensors to calculate the vehicle distance at a very high cost. In this paper, we propose a method based on deep learning to calculate the vehicle distance with a monocular camera. Our method is inexpensive and quite convenient to deploy on the mobile platforms. This paper makes two contributions. First, based on Light-Head RCNN, we propose a new vehicle detection framework called Light-Car Detection which can be used on the mobile platforms. Second, the planar homography of projective geometry is used to calculate the distance between the camera and the vehicles ahead. The results show that our detection system achieves 13FPS detection speed and 60.0% mAP on the Adreno 530 GPU of Samsung Galaxy S7, while only requires 7.1MB of storage space. Compared with the methods existed, the proposed method achieves a better performance.

• Journal of Multimedia Information System
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• v.1 no.2
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• pp.119-126
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• 2014

We propose a bicycle detection system riding on people based on modified projected local binary pattern(PLBP) for vision based intelligent vehicles. Projection method has robustness for rotation invariant and reducing dimensionality for original image. The features of Local binary pattern(LBP) are fast to compute and simple to implement for object recognition and texture classification area. Moreover, We use uniform pattern to remove the noise. This paper suggests that modified LBP method and projection vector having different weighting values according to the local shape and area in the image. Also our system maintains the simplicity of evaluation of traditional formulation while being more discriminative. Our experimental results show that a bicycle and motorcycle riding on people detection system based on proposed PLBP features achieve higher detection accuracy rate than traditional features.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.45-54
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• 2018

This paper presents a vision-based fall detection system to automatically monitor and detect people's fall accidents, particularly those of elderly people or patients. For video analysis, the system should be able to extract both spatial and temporal features so that the model captures appearance and motion information simultaneously. Our approach is based on 3-dimensional convolutional neural networks, which can learn spatiotemporal features. In addition, we adopts a thermal camera in order to handle several issues regarding usability, day and night surveillance and privacy concerns. We design a pan-tilt camera with two actuators to extend the range of view. Performance is evaluated on our thermal dataset: TCL Fall Detection Dataset. The proposed model achieves 90.2% average clip accuracy which is better than other approaches.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.10
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• pp.5219-5223
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• 2012

Osteopontin (OPN) is an integrin-binding protein, believed to be involved in a variety of physiological cellular functions. The physiology of OPN is best documented in the bone where this secreted adhesive glycoprotein appears to be involved in osteoblast differentiation and bone formation. In our study, we used semi-quantitative RT-PCR of osteopontin in calcification tissue of breast to detect breast cancer metastasis. The obtained data indicate that the expression of osteopontin is related to calcification tissue of breast, and possibly with the incidence of breast cancer. The expression strength of OPN by RT-PCR detection was related to the degree of malignancy of breast lesions, suggesting a close relationship between OPN and breast calcification tissue. The results revealed that expression of OPN mRNA is related to calcification of breast cancer tissue and to the development of breast cancer. Determination of OPN mRNA expression can be expected to be a guide to clinical therapy and prediction of the prognosis of breast cancer patients.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.1051-1058
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• 2018

This paper propose image processing algorithm to prevent safety accidents near by subway platform screen door(PSD). First, edges of the subway PSD images are detected and the boundary line between PSD and subway platform is detected to decide people's approach to the PSD using Hough transform. To do this, we draw the boundary line between the PSD and platform, to detect the boundary line and to decide the people's approach to the detected line is completely connected or not. Generally, edge is the basic characteristic of image; thus, edge detection is very important in image processing applications and computer vision area. The conventional edge detection methods such as Roberts, Sobel, Prewitt, and Laplacian etc, which are using a fixed value of mask, and morphological gradient from the structuring element of view and Canny edge detector are widely used. In this paper, we propose the detection algorithm about the people's approach to the subway PSD to prevent the safety accidents by using Canny edge detector and Hough transform and the computer simulation shows the results.