• Journal of International Society for Simulation Surgery
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• v.3 no.1
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• pp.1-8
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• 2016

In the field of Radiology, the Computer Aided Diagnosis is the technology which gives valuable information for surgical purpose. For its importance, several computer vison methods are processed to obtain useful information of images acquired from the imaging devices such as X-ray, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). These methods, called pattern recognition, extract features from images and feed them to some machine learning algorithm to find out meaningful patterns. Then the learned machine is then used for exploring patterns from unseen images. The radiologist can therefore easily find the information used for surgical planning or diagnosis of a patient through the Computer Aided Diagnosis. In this paper, we present a review on three widely-used methods applied to Computer Aided Diagnosis. The first one is the image processing methods which enhance meaningful information such as edge and remove the noise. Based on the improved image quality, we explain the second method called segmentation which separates the image into a set of regions. The separated regions such as bone, tissue, organs are then delivered to machine learning algorithms to extract representative information. We expect that this paper gives readers basic knowledges of the Computer Aided Diagnosis and intuition about computer vision methods applied in this area.

• Tuberculosis and Respiratory Diseases
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• v.57 no.6
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• pp.515-521
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• 2004

With the increasing resolution of modern CT scanners, analysis of the larger numbers of images acquired in a lung screening exam or diagnostic study is necessary, which also needs high accuracy and reproducibility. Recent developments in the computerized analysis of medical images are expected to aid radiologists and other healthcare professional in various diagnostic tasks of medical image interpretation. This article is to provide a brief overview of some of computer-aided diagnosis schemes in chest CT.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.599-602
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• 2004

최근 국내 여성의 유방암 발생율은 1위를 차지하며 그 비율 또한 나날이 늘어가고 있는 추세이다. 하지만 유방암은 다른 암에 비해 5년간 관찰 생존율이 약 76%로 갑상선에 이어 두 번째의 생존율을 보이며, 이는 조기발견의 중요성을 다시 한번 상기시키게 한다. 하지만 국내에서 사용되는 유방암 조기검진 방법에는 Mammography(유방촬영술)와 초음파 진단 두 가지가 주를 이루고 있으나 촬영과정 및 장비에 따른 오차로 인한 객관화된 정보생성 부족 및 전달의 부족으로 문제점이 대두되고 있다. 본 연구에서는 Mamography 및 초음파 유방 진단술을 이용하여 전문의의 의사결정에 도움을 줄 수 있는 CAD(Computer Aided Diagnosis) 시스템의 유방암 진단의 특징을 이용, 전문의 관점의 모델링을 기술해보고자 한다.

• Journal of Convergence Society for SMB
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• v.6 no.4
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• pp.79-84
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• 2016

A novel diagnosis scheme PCADP(personalized computer aided diagnosis probability) is proposed to overcome the problems mentioned above. PCADP scheme is a personalized diagnosis method based on ontology and it makes the bio-data analysis just a 'process' in the Smart healthcare service system. In addition, we offer a semantics modeling of the smart healthcare ontology framework in order to describe smart healthcare data and service specifications as meaningful representations based on this PCADP. The PCADP scheme is a kind of statistical diagnosis method which has real-time processing, characteristics of flexible structure, easy monitoring of decision process, and continuous improvement.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.131-136
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• 2018

This paper proposes a computer-aided diagnostic algorithm in a non-invasive way. Currently, clinical diagnosis of jaundice is performed through blood sampling. Unlike the old methods, the non-invasive method will enable parents to measure newborns' jaundice by only using their mobile phones. The proposed algorithm enables high accuracy and quick diagnosis through machine learning. In here, we used the SVM model of machine learning that learned the feature extracted through image preprocessing and we used the international jaundice research data as the test data set. As a result of applying our developed algorithm, it took about 5 seconds to diagnose jaundice and it showed a 93.4% prediction accuracy. The software is real-time diagnosed and it minimizes the infant's pain by non-invasive method and parents can easily and temporarily diagnose newborns' jaundice. In the future, we aim to use the jaundice photograph of the newborn babies' data as our test data set for more accurate results.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.13-18
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• 2004

최근 몇 년간 방사선 의학진단과 관련된 연구가 한층 높아진 가운데 유방암은 여성의 암 중에서 1위를 차지하고 조기에 진단하고 치료하기 위한 국가적인 노력이 필요한 시점이다. 이렇듯 여성들의 유방암 발생빈도수가 급증하면서 대두 되고 있는 것이 조기 진단방법인 Mammography와 초음파 진단이며 그로인하여 발생하는 오진률 역시 많은 연구가 진행 되고 있다. 먼저 Mammography 및 초음파 진단의 문제점 보면 첫째 촬영과정에서의 오차, 둘째 영상의 선명도 ,셋째 전문의의 판독에 대한오차, 넷째 의사의 경험으로 진단함으로 표준화가 존재하지 않는다는 공통적인 문제점을 가지고 있다. 본 연구에서는 CAD 시스템의 프레임웍 및 요소 기술을 제시하여 의사의 진단을 보조적 수행이 보다 수월하도록 하고자 한다. 본 연구에서는 CAD시스템의 기능은 Detection기능(Image enhancement, Morphology, segment detection)과 Diagnosis기능( Neural Natwork등을 이용하여 증상을 판단)이다. 또한 과거 자료를 이용한 변이 및 변화를 예측함으로써 향후 있을 위험요소에 대비가 가능한 모듈과 전문의사가 대화형으로 빠르게 진단지식을 구축할 수 있는 지능형, 대화형 온라인 진단기능을 추가함으로써 외국의 CAD시스템과는 많은 차이가 있다고 볼 수 있다.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.7
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• pp.3651-3658
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• 2016

Computer-aided diagnosis of breast cancer is an important medical approach. In this research paper, we focus on combining two major methodologies, namely fuzzy base systems and the evolutionary genetic algorithms and on applying them to the Saudi Arabian breast cancer diagnosis database, to aid physicians in obtaining an early-computerized diagnosis and hence prevent the development of cancer through identification and removal or treatment of premalignant abnormalities; early detection can also improve survival and decrease mortality by detecting cancer at an early stage when treatment is more effective. Our hybrid algorithm, the genetic-fuzzy algorithm, has produced optimized systems that attain high classification performance, with simple and readily interpreted rules and with a good degree of confidence.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.307-310
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• 2021

In the recent years, studies using Computer-Aided Diagnostics(CAD) have been actively conducted, such as signal and image processing technology using breast ultrasound images, automatic image optimization technology, and automatic detection and classification of breast masses. As computer diagnostic technology is developed, it is expected that early detection of cancer will proceed accurately and quickly, reducing health insurance and test ice for patients, and eliminating anxiety about biopsy. In this paper, a quantitative analysis of tumors was conducted in ultrasound images using a gray level co-occurrence matrix(GLCM) to experiment with the possibility of use for computer assistance diagnosis.

• The Journal of the Korea Contents Association
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• v.11 no.11
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• pp.185-193
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• 2011

There is no exact standard of detecting pulmonary tuberculosis(TB) in digital image of simple chest radiography. In this study, I experimented on the principal components analysis(PCA) algorithm in the past and suggested six other parameters as identification of TB lesions. The purpose of this study was to develop and test computer aided diagnosis(detection) method for the detection and measurement of pulmonary abnormalities on digital chest radiography. It showed comparatively low recognition diagnosis rate using PCA method, however, six kinds of texture features parameters algorithm showed similar or higher diagnosis rates of pulmonary disease than that of the clinical radiologists. Proposed algorithms using computer-aided of texture analysis can distinguish between areas of abnormality in the chest digital images, differentiate lesions having pulmonary disease. The method could be useful tool for classifying and measuring chest lesions, it would play a major role in radiologist's diagnosis of disease so as to help in pre-reading diagnosis and prevention of pulmonary tuberculosis.