• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.8
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• pp.1424-1429
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• 2016

Segmentation is one of the first steps in most diagnosis systems for characterization of dental caries in an early stage. The purpose of automatic dental cavity detection system is helping dentist to make more precise diagnosis. We proposed the semi-automatic method for the segmentation of dental caries on digital x-ray images. Based on a manually and roughly selected ROI (Region of Interest), it calculated the contour for the dental cavity. A snake algorithm which is one of active contour models repetitively refined the initial contour and self-examination and correction on the segmentation result. Seven phantom tooth from incisor to molar were made for the evaluation of the developed algorithm. They contained a different form of cavities and each phantom tooth has two dental cavities. From 14 dental cavities, twelve cavities were accurately detected including small cavities. And two cavities were segmented partly. It demonstrates the practical feasibility of the dental lesion detection using Computer-aided Detection (CADe).

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.69-77
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• 2018

The malignant melanoma accounts for about 1 to 3% of the total malignant tumor in the West, especially in the US, it is a disease that causes more than 9,000 deaths each year. Generally, skin lesions are difficult to detect the features through photography. In this paper, we propose a computer-aided diagnosis algorithm based on deep learning for classification of malignant melanoma and benign skin tumor in RGB channel skin images. The proposed deep learning model configures the tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to segment a skin lesion area in the dermoscopic image. We could implement algorithms to classify malignant melanoma and benign tumor using skin lesion image and results of expert's labeling in ResNet. The U-Net model obtained a dice similarity coefficient of 83.45% compared with results of expert's labeling. The classification accuracy of malignant melanoma obtained the 83.06%. As the result, it is expected that the proposed artificial intelligence algorithm will utilize as a computer-aided diagnosis algorithm and help to detect malignant melanoma at an early stage.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1212-1223
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• 2022

Skin lesions are common diseases that range from skin rashes to skin cancer, which can lead to death. Note that early diagnosis of skin diseases can be important because early diagnosis of skin diseases considerably can reduce the course of treatment and the harmful effect of the disease. Recently, the development of computer-aided diagnosis (CAD) systems based on artificial intelligence has been actively made for the early diagnosis of skin diseases. In a typical CAD system, the accurate classification of skin lesion types is of great importance for improving the diagnosis performance. Motivated by this, we propose a novel deep ensemble classification with multi-scale attention networks. The proposed deep ensemble networks are jointly trained using a single loss function in an end-to-end manner. In addition, the proposed deep ensemble network is equipped with a multi-scale attention mechanism and segmentation information of the original skin input image, which improves the classification performance. To demonstrate our method, the publicly available human skin disease dataset (HAM 10000) and the private animal skin lesion dataset were used for the evaluation. Experiment results showed that the proposed methods can achieve 97.8% and 81% accuracy on each HAM10000 and animal skin lesion dataset. This research work would be useful for developing a more reliable CAD system which helps doctors early diagnose skin diseases.

• Biomedical Science Letters
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• v.29 no.2
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• pp.53-57
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• 2023

The main objective of pathologists is to achieve accurate lesion diagnoses, which has become increasingly challenging due to the growing number of pathological slides that need to be examined. However, using digital technology has made it easier to complete this task compared to older methods. Digital pathology is a specialized field that manages data from digitized specimen slides, utilizing image processing technology to automate and improve analysis. It aims to enhance the precision, reproducibility, and standardization of pathology-based researches, preclinical, and clinical trials through the sophisticated techniques it employs. The advent of whole slide imaging (WSI) technology is revolutionizing the pathology field by replacing glass slides as the primary method of pathology evaluation. Image processing technology that utilizes WSI is being implemented to automate and enhance analysis. Artificial intelligence (AI) algorithms are being developed to assist pathologic diagnosis and detection and segmentation of specific objects. Application of AI-based digital pathology in biomedical researches is classified into four areas: diagnosis and rapid peer review, quantification, prognosis prediction, and education. AI-based digital pathology can result in a higher accuracy rate for lesion diagnosis than using either a pathologist or AI alone. Combining AI with pathologists can enhance and standardize pathology-based investigations, reducing the time and cost required for pathologists to screen tissue slides for abnormalities. And AI-based digital pathology can identify and quantify structures in tissues. Lastly, it can help predict and monitor disease progression and response to therapy, contributing to personalized medicine.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.529-536
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• 2022

In the past two years, Severe Acute Respiratory Syndrome Coronavirus-2(SARS-CoV-2) has been hitting more and more to people. This paper proposes a novel U-Net Convolutional Neural Network to classify and segment COVID-19 lung CT images, which contains Sub Coding Block (SCB), Atrous Spatial Pyramid Pooling(ASPP) and Attention Gate(AG). Three different models such as FCN, U-Net and U-Net-SCB are designed to compare the proposed model and the best optimizer and atrous rate are chosen for the proposed model. The simulation results show that the proposed U-Net-MMFE has the best Dice segmentation coefficient of 94.79% for the COVID-19 CT scan digital image dataset compared with other segmentation models when atrous rate is 12 and the optimizer is Adam.

• Journal of the Korean Society of Radiology
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• v.18 no.1
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• pp.21-26
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• 2024

The one of the famous deep learning models for object detection task is you only look once version 5 (YOLOv5) framework based on the one stage architecture. In addition, YOLOv5 model indicated high performance for accurate lesion detection using the bottleneck CSP layer and skip connection function. The purpose of this study was to evaluate the performance of YOLOv5 framework according to various hyperparameters in position emission tomogrpahy (PET) phantom images. The dataset was obtained from QIN PET segmentation challenge in 500 slices. We set the bounding box to generate ground truth dataset using labelImg software. The hyperparameters for network train were applied by changing optimization function (SDG, Adam, and AdamW), activation function (SiLU, LeakyRelu, Mish, and Hardwish), and YOLOv5 model size (nano, small, large, and xlarge). The intersection over union (IOU) method was used for performance evaluation. As a results, the condition of outstanding performance is to apply AdamW, Hardwish, and nano size for optimization function, activation function and model version, respectively. In conclusion, we confirmed the usefulness of YOLOv5 network for object detection performance in nuclear medicine images.

• Journal of radiological science and technology
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• v.35 no.2
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• pp.133-139
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• 2012

For the examination of nuclear medicine, myocardial scan is a good method to evaluate a hemodynamic importance of coronary heart disease. but, the automatized qualitative measurement is additionally necessary to improve the decoding efficiency. we suggests the creation of cardiac three-dimensional model and model of three-dimensional cardiac thickness as a new measurement. For the experiment, cardiac reduced cross section was obtained from SPECT. Next, the pre-process was performed and image segmentation was fulfilled by level set. for the modeling of left cardiac thickness, it was realized by applying difference equation of two-dimensional laplace equation. As the result of experiment, it was successful to measure internal wall and external wall and three-dimensional modeling was realized by coordinate. and, with laplace formula, it was successful to develop the thickness of cardiac wall. through the three-dimensional model, defects were observed easily and position of lesion was grasped rapidly by the revolution of model. The model which was developed as the support index of decoding will provide decoding information to doctor additionally and reduce the rate of false diagnosis as well as play a great role for diagnosing IHD early.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.163-169
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• 2016

In Korea, prostate cancer accounted for generating growth rate second the following thyroid cancer, because of western dietary habits. Survival rate of prostate cancer after clinical behavior is changed depend on follow-up management. A telemedicine have been applied to replacement of medical specialist in rural area, and a quick reaction to emergency situation. Our study developed prostate 3-dimensional (3D) visualization program and designed prostate aftercare system architecture, called smart care, using a device that can access the Internet. Region of interest (ROI) in prostate was manually segmented by physicians and visualized to 3D objects and sent to PACS Server as DICOM images. So, medical personnel could confirm patients' data along with 3D images not only PACS system, but also portable device like a smart phone. As a result, we conducted the aftercare service to 98 patients and visualize 3D prostate images. 3D images had advantage to instinctively apprehend where lesion is and make patients to understand state of their disease easily. In the future, should conduct an aftercare service to more patients, and will obtain numerical index through follow-up study to an accurate analysis.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.5
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• pp.3045-3050
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• 2013

Purpose: Hepatic resection is arguably the preferred treatment for huge hepatocellular carcinoma (H-HCC). Estimating the remnant liver volume is therefore essential. This study aimed to evaluate the feasibility of using computer-assisted volumetric analysis for this purpose. Methods: The study involved 40 patients with H-HCC. Laboratory examinations were conducted, and a contrast CT-scan revealed that 30 cases out of the participating 40 had single-lesion tumors. The remaining 10 had less than three satellite tumors. With the consensus of the team, two physicians conducted computer-assisted 3D segmentation of the liver, tumor, and vessels in each case. Volume was automatically computed from each segmented/labeled anatomical field. To estimate the resection volume, virtual lobectomy was applied to the main tumor. A margin greater than 1 cm was applied to the satellite tumors. Resectability was predicted by computing a ratio of functional liver resection (R) as (Vresected-Vtumor)/(Vtotal-Vtumor) x 100%, applying a threshold of 50% and 60% for cirrhotic and non-cirrhotic cases, respectively. This estimation was then compared with surgical findings. Results: Out of the 22 patients who had undergone hepatectomies, only one had an R that exceeded the threshold. Among the remaining 18 patients with non-resectable H-HCC, 12 had Rs that exceeded the specified ratio and the remaining 6 had Rs that were < 50%. Four of the patients who had Rs less than 50% underwent incomplete surgery due to operative findings of more extensive satellite tumors, vascular invasion, or metastasis. The other two cases did not undergo surgery because of the high risk involved in removing the tumor. Overall, the ratio of functional liver resection for estimating resectability correlated well with the other surgical findings. Conclusion: Efficient pre-operative resectability assessment of H-HCC using computer-assisted volumetric analysis is feasible.