• Asian Pacific Journal of Cancer Prevention
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• 제17권3호
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• pp.1049-1052
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• 2016

Background: Myelodysplastic syndrome (MDS) is a clonal disorder of hemopoeitic stem cells, characterized by infective hematopoiesis, peripheral cytopenias along with hypercellularity of marrow and marked dysplastic features. Our aim was to study the spectrum of the WHO classification in adult Pakistani patients with MDS at disease presentation. Materials and Methods: This retrospective descriptive study was conducted at Liaquat National Hospital and Medical College, extending from January 2010 to December 2014. Patient data were retrieved from the maintained archives. Results: Overall, 45 patients were diagnosed at our institution with de novo MDS during the study period. There were 28 males and 17 females. Age ranged between 18 and 95 years with a mean of $57.6{\pm}17.4years$. The male to female ratio was 1.7:1. According to the WHO classification, 53.3% had refractory cytopenia with multilineage dysplasia, 22.2% had refractory cytopenia with unilineage dysplasia, 4.4% each had refractory anemia with excess of blasts-1 and II and 15.5% had MDS unclassified. The main presenting complaints were generalized fatigue (60%), fever (33.3%), dyspnea (15.5%), bleeding (13.3%) and weight loss (11.1%). Physical examination revealed pallor in 37.7%, followed by petechial and purpuric rashes in 20% of patients. Hemoglobin was <10 g/dl in 41 (91.1%). Pancytopenia and bicytopenia were noted in 18 (40%) and 14 (31.1%) respectively. Conclusions: MDS in our patients presents at a relatively young age. Refractory c ytopenia with multilineage dysplasia was the dominant disease variant in our setting.

• 한국정보전자통신기술학회논문지
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• 제5권3호
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• pp.144-151
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• 2012

한국에서 만성위염은 10명당 한 명 꼴로 발생하는 질병이다. 서양의학에서는 만성위염을 진단하기 위해서 내시경 조사를 하지만 이는 환자에게 고통을 주고 비용이 비싸다는 단점을 가지고 있다. 반면 전통한방의학에 따르면, 오른쪽 손목의 '관' 위치는 위와 관련이 있다. 따라서 오른쪽 손목의 '관' 위치의 맥파를 측정하면 만성위염을 진단할 수 있을 것이다. 하지만 맥진은 한의사들의 지식과 경험에 의존하고 있다. 본 연구에서는 맥파를 분석하기 위한 체계적인 접근 방법을 제안한다. 처음에 맥파는 전처리 과정을 거친다. 그 다음 맥파에 가우시안 모델을 적용시킨 후, 맥파의 주요 인자들을 추출한다. 그리고 t-검증과 통계적 차이를 이용하여 질병에 민감한 파라미터들을 선택한다. 마지막으로 선택한 파라미터들은 분류를 위해서 Fuzzy C-Means (FCM) 알고리즘에 입력된다. 분류 결과 건강한 사람은 95% 만성위염 환자는 87% 분류하였다.

• 한국방사선학회논문지
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• 제17권5호
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• pp.735-742
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• 2023

비알콜성 지방간은 심혈관계 질환, 당뇨병, 고혈압 및 신장질환의 발생에 있어 독립적인 위험인자에 해당하며, 최근에는 비알콜성 지방간에 대한 임상적 중요성이 증가하고 있다. 본 연구에서는 비알콜성 지방간 환자의 초음파영상에 대하여 질감분석 방법인 GLCM을 적용하여 특징값을 추출하고자 한다. 추출된 특징값들을 이용한 인공신경망 모델의 적용을 통하여 비알콜성 지방간의 지방침착 정도를 정상 간(normal), 경도 지방간(mild), 중등도 지방간(moderate), 중증 지방간(severe)으로 분류를 하고자 한다. GLCM알고리듬 적용 결과 Autocorrelation, Sum of squares, Sum average, Sum variance 파라미터 값들은 경도 지방간, 중등도 지방간을 거쳐 중증 지방간으로 갈수록 특징값의 평균값이 증가하는 경향성을 나타내었다. 인공신경망 모델의 입력은 비알콜성 지방간질환의 초음파영상에 GLCM 알고리듬을 적용하여 추출한 Autocorrelation, Sum of squares, Sum average, Sum variance의 4개의 파라미터들을 인공신경망 모델의 입력값으로 적용하였다. 비알콜성 지방간질환의 초음파영상에 GLCM 알고리듬을 적용하여 추출한 영상을 인공신경망에 적용하여 분류 정확도를 평가한 결과 92.5%의 높은 정확도를 나타내었다. 이러한 결과를 통하여 비알콜성 지방간 환자의 초음파 영상에 대한 질감 분석 GLCM 연구 시 본 연구의 결과를 기초자료로 제시를 하고자 한다.

• 한국컴퓨터정보학회논문지
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• 제18권9호
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• pp.43-51
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• 2013

신뢰성 있는 부정맥 진단을 위해서는 리듬 구간 및 심박 단위의 종합적인 분석을 통하여 심전도 신호에 대한 분류 결과가 제시되어야 한다. 본 논문에서는 심전도 신호의 특징점에 기반하여 규칙기반 분류를 이용한 일정 구간의 리듬 분석을 수행하고 SVM기반 분류를 이용한 심박 단위의 리듬분석을 첨가하였다. 규칙기반 분류에서는 리듬 구간의 특징에 대하여 임상 자료로부터 도출된 규칙 베이스를 이용하여 리듬 유형을 분류하도록 하며, SVM기반 분류에서는 심박 단위의 특징에 대하여 미리 학습된 다중 SVM 분류기를 이용하여 단조 리듬 및 주요 비정상 심박을 분류하도록 한다. MIT-BIH 부정맥 데이터베이스를 이용한 실험을 통하여 11가지 리듬 유형에 대하여 규칙기반 방법만을 적용하였을 경우 68.52%, 규칙기반과 SVM기반의 융합 방법을 적용하였을 경우 87.04%의 분류 성능을 각각 보였다. SVM기반 방법으로 단조 리듬과 배열 리듬에 대한 오분류 개선을 통하여 분류 성능에서 19% 정도가 향상됨을 확인하였다.

• 한방안이비인후피부과학회지
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• 제16권1호
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• pp.1-32
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• 2003

Objectives: This study was carried out to analyze and understand the trends of research papers published In the Journal of Oriental Medical Surgery？phthalmology & Otolaryngology Society(JOMSOOS). Methods: We studied the 308 research papers that had been published in JOMSOOS from 1988 Vol. 1. No. 1. to 2002 Vol. 15. No. 2. Our study was developed through the four stages in order to analyze the papers; 1) we analyzed all the papers overall to classify them into three categories; original article. review article and case report. 2) we classified the original articles in terms of methodology. 3) we also analyzed the case reports according to the sort of disease each paper dealt with. 4) we had another statistical approach to each paper to figure out the distribution of diagnoses in detail. Results: We have got the following outcomes from our analysis of the papers in terms of the four stages. 1. Overall Analysis. 1) Classification of 308 research papers between 1988 and 2002: 137 original articles(44.48$\%$), 111 review articles(36.04$\%$), 56 case reports(18.18$\%$). 2) Used language: Korean(99.03$\%$). English(0.97$\%$). 3) The Number of Authors: 2 persons(42.86$\%$). 3 persons(29.87$\%$), 1 person(14.61$\%$). 2. Original Article Analysis 1) Classification of 137 original articles in terms of methodology: 90 experimental studies(65.69$\%$)46 descriptive studies(33.58$\%$), 1 analytic study(0.73$\%$). 2) Classification of the original articles according to the use of statistical methods: No statistical methods(36.42$\%$), Descriptive methods only(1.99$\%$), Not defined(23.18$\%$), t-test(24.50$\%$), ANOVA(3.97$\%$), Multiple comparison(2.65$\%$), Non-parametric test(2.65$\%$), Other methods(1.32$\%$). 3) Classification of 46 descriptive articles in terms of diseases: otorhinolaryngology(43.48$\%$), dermatology(23.91$\%$), ophthalmology(13.04$\%$), facial palsy(13.04$\%$). 4) Classification of descriptive articles in terms of the number of patients: the highest was 'more than 26 but less than 50 persons'(19 articles - 41.30$\%$). 5) Classification of descriptive articles in terms of the period for patients observation: the highest was the time 'more than 9 but less than 12 months(34.78$\%$)' Out of the 34.78$\%$, the number of articles with the patients observed for more than 12 months was 13(28.26$\%$). 3. Case Report Analysis 1) Classification of 56 case reports in terms of the sort of disease: dermatology(44.64$\%$), ophthalmology(19.64$\%$), otorhinolaryngology(14.29$\%$), facial palsy(8.93$\%$). 2) Classification in terms of the number of patients: 1 person(50$\%$), 3 persons(16.07$\%$), 2 persons(14.29$\%$). 4. Diagnosis Distribution of Each Disease. 1) Studies regarding ophthalmology : the percentage of 'strabismus' cases was the highest(33.33$\%$). 2) Studies regarding otorhinolaryngology : nasal inflammation(34.48$\%$), tinnitus(20.69$\%$). 3) Studies regarding dermatology: the percentage of 'allergic skin disease' was the highest(33.33$\%$). Conclusions: We analyzed the trends of research papers that have been published in JOMSOOS in detail. We came to understand the trends of the research through this study. However, we acknowledge that we only adopted the quantitative method out of various possible analysis methods. For further studies, we strongly urge to adopt the qualitative methods as well.

• Imaging Science in Dentistry
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• 제32권3호
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• pp.129-134
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• 2002

Purpose : To implement the database system of oral and maxillofacial radiological images using a commercial medical image management software with personally developed classification code. Materials and methods : The image database was built using a slightly modified commercial medical image management software, Dr. Image v.2.1 (Bit Computer Co., Korea). The function of wild card '*' was added to the search function of this program. Diagnosis classification codes were written as the number at the first three digits, and radiographic technique classification codes as the alphabet right after the diagnosis code. 449 radiological films of 218 cases from January, 2000 to December, 2000, which had been specially stored for the demonstration and education at Dept. of OMF Radiology of Dankook University Dental Hospital, were scanned with each patient information. Results: Cases could be efficiently accessed and analyzed by using the classification code. Search and statistics results were easily obtained according to sex, age, disease diagnosis and radiographic technique. Conclusion : Efficient image management was possible with this image database system. Application of this system to other departments or personal image management can be made possible by utilizing the appropriate classification code system.

• 한국멀티미디어학회논문지
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• 제21권10호
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• pp.1150-1161
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• 2018

Classification is widely used in medical images to categorize patients and non-patients. However, conventional classification requires a complex procedure, including some rigid steps such as pre-processing, segmentation, feature extraction, detection, and classification. In this paper, we propose a novel convolutional neural network (CNN), called LeukemiaNet, to specifically classify two different types of leukemia, including acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), and non-cancerous patients. To extend the limited dataset, a PCA color augmentation process is utilized before images are input into the LeukemiaNet. This augmentation method enhances the accuracy of our proposed CNN architecture from 96.9% to 97.2% for distinguishing ALL, AML, and normal cell images.

• 전기전자학회논문지
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• 제25권1호
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• pp.212-217
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• 2021

Premature contraction arrhythmia is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Most of arrhythmia clasification methods have been developed with the primary objective of the high detection performance without taking into account the computational complexity. Also, personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Therefore it is necessary to design efficient method that classifies arrhythmia by analyzing the persons's physical condition and decreases computational cost by accurately detecting minimal feature point based on only QRS features. We propose method for personalized specific classification of premature contraction arrhythmia based on QRS features in smart healthcare environments. For this purpose, we detected R wave through the preprocessing method and SOM and selected abnormal signal sets.. Also, we developed algorithm to classify premature contraction arrhythmia using QRS pattern, RR interval, threshold for amplitude of R wave. The performance of R wave detection, Premature ventricular contraction classification is evaluated by using of MIT-BIH arrhythmia database that included over 30 PVC(Premature Ventricular Contraction) and PAC(Premature Atrial Contraction). The achieved scores indicate the average of 98.24% in R wave detection and the rate of 97.31% in Premature ventricular contraction classification.

• International Journal of Computer Science & Network Security
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• 제21권5호
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• pp.267-274
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• 2021

Medical care practices include gathering a wide range of student data that are with manic episodes and depression which would assist the specialist with diagnosing a health condition of the students correctly. In this way, the instructors of the specific students will also identify those students and take care of them well. The data which we collected from the students could be straightforward indications seen by them. The artificial intelligence has been utilized with Naive Baye's classification, Random forest classification algorithm, SVM algorithm to characterize the datasets which we gathered to check whether the student is influenced by Bipolar illness or not. Performance analysis of the disease data for the algorithms used is calculated and compared. Also, a sequential deep learning model is builded using Keras. The consequences of the simulations show the efficacy of the grouping techniques on a dataset, just as the nature and complexity of the dataset utilized.

• International journal of advanced smart convergence
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• 제1권1호
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• pp.43-47
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• 2012

Parkinson's disease is representative degenerative diseases of the nervous system, which is from deficiency of dopamine neurons to pass in which the gradual degeneration of the body. In this paper, open UCI repository data of Parkinson's patients is used for experiments. The classification based on correlation analysis is examined. In addition, the relationship between groups is differentiated by cluster analysis based on patients with Parkinson's disease by apriori algorithm and correlation analysis. It is used to find the properties that distinguish cluster analysis. Though the disease is the same in the basic structure, each group is compared as each gender group with the most distinctive part of the characteristics.