• Asian Pacific Journal of Cancer Prevention
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• 제17권11호
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• pp.4869-4873
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• 2016

Colonoscopy is currently the best technique available for the detection of colon cancer or colorectal polyps or other precursor lesions. Computer aided detection (CAD) is based on very complex pattern recognition. Local binary patterns (LBPs) are strong illumination invariant texture primitives. Histograms of binary patterns computed across regions are used to describe textures. Every pixel is contrasted relative to gray levels of neighbourhood pixels. In this study, colorectal polyp detection was performed with colonoscopy video frames, with classification via J48 and Fuzzy. Features such as color, discrete cosine transform (DCT) and LBP were used in confirming the superiority of the proposed method in colorectal polyp detection. The performance was better than with other current methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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• pp.860-880
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• 2024

Breast cancer is a major health concern affecting women and men globally. Early detection and accurate classification of breast cancer are vital for effective treatment and survival of patients. This study addresses the challenge of accurately classifying breast tumors using machine learning classifiers such as MLP, AdaBoostM1, logit Boost, Bayes Net, and the J48 decision tree. The research uses a dataset available publicly on GitHub to assess the classifiers' performance and differentiate between the occurrence and non-occurrence of breast cancer. The study compares the 10-fold and 5-fold cross-validation effectiveness, showing that 10-fold cross-validation provides superior results. Also, it examines the impact of varying split percentages, with a 66% split yielding the best performance. This shows the importance of selecting appropriate validation techniques for machine learning-based breast tumor classification. The results also indicate that the J48 decision tree method is the most accurate classifier, providing valuable insights for developing predictive models for cancer diagnosis and advancing computational medical research.

• 대한의용생체공학회:의공학회지
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• 제30권2호
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• pp.122-128
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• 2009

Cancer serum biomarkers have advanced our ability to more accurately predict tumor classification, prognostic/metastatic potential, and response potential to novel chemotherapies. Serum amyloid A (SAA) and Vascular endothelial growth factor (VEGF) have potential utility as a serum biomarker for lung cancer. Quantum dots, nanometer-sized crystals, have a high quantum yield, sensitivity, and pronounced photostability. The properties of quantum dots can be efficiently applied to the detection of serum biomarkers in immunoassays as fluorescent probe. We used quantum dots as fluorescent probes in immunoassays and attempted to detect serum amyloid A and vascular endothelial growth factor as serum biomarkers of lung cancer. This fluorescence immunoassay based on the properties of quantum dots is applicable to the detection of serum biomarkers for lung cancer. The fluorescence immunoassay with quantum dots should allow the efficient and specific detection of serum amyloid A (SAA) for the possible diagnosis of lung cancer.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.255-256
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• 2006

Raman spectroscopy has strong potential for providing noninvasive dermatological diagnosis of skin cancer. In this study, we investigated two classification methods with maximum a posteriori (MAP) probability and multi-layer perceptron (MLP) classification. The classification framework consists of preprocessing of Raman spectra, feature extraction, and classification. In the preprocessing step, a simple windowing method is proposed to obtain robust features. Classification results with MLP involving 216 spectra preprocessed with the proposed method gave 97.3% sensitivity, which is very promising results for automatic Basal Cell Carcinoma (BCC) detection.

• 실천공학교육논문지
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• 제16권1_spc호
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• pp.59-64
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• 2024

고령화가 심화되면서 암 발병률이 증가하고 있다. 피부 암은 외적으로 보이지만 사람들이 알아채지 못하거나 가볍게 간과하는 경우가 많다. 이에 초기 발견 시기를 놓쳐 말기의 경우 생존율이 7.5~11%로 사망에 이를 수 있다. 하지만 피부 암을 진단함에 있어 육안으로 진단하는 것이 아닌 정밀검사, 세포 검사 등 시간과 비용이 많이 든다는 단점이 있다. 따라서 본 연구에서는 이러한 단점을 해결하기 위해 Attention CNN 모델 기반 피부암 분류 시스템을 제안한다. 이 시스템은 전문의로 하여금 피부 암을 초기에 발견하여 신속한 조치를 취할 수 있도록 하는데 큰 도움을 줄 수 있다. 피부암 종류에 따른 이미지 데이터 불균형 문제에서 분포 비율이 낮은 데이터에는 Over Sampling 기법을, 분포 비율이 높은 데이터에는 Under Sampling 기법을 적용하여 완화하고 Attention layer가 없는 모델과 있는 모델을 비교하여 Attention layer가 없는 사전학습 모델에 추가한 피부암 분류 모델을 제안한다. 또한, 특정 클래스에 대하여 데이터 증강 기법을 강화하여 데이터 불균형 문제를 해결할 계획이다.

• Asian Pacific Journal of Cancer Prevention
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• 제16권10호
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• pp.4237-4245
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• 2015

Breast cancer is a menacing cancer, primarily affecting women. Continuous research is going on for detecting breast cancer in the early stage as the possibility of cure in early stages is bright. There are two main objectives of this current study, first establish statistics for breast cancer and second to find methodologies which can be helpful in the early stage detection of the breast cancer based on previous studies. The breast cancer statistics for incidence and mortality of the UK, US, India and Egypt were considered for this study. The finding of this study proved that the overall mortality rates of the UK and US have been improved because of awareness, improved medical technology and screening, but in case of India and Egypt the condition is less positive because of lack of awareness. The methodological findings of this study suggest a combined framework based on data mining and evolutionary algorithms. It provides a strong bridge in improving the classification and detection accuracy of breast cancer data.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.396-398
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• 2022

위장관의 내시경적 병리학적 소견은 대장암의 조기 진단에 중요하다. 최근 CNN 기반 딥 러닝의 활용은 주관적 분석의 정확도와 조기 진단의 성능을 높이는 결과를 보였으나, 계산 복잡도가 높고 임상에 즉시 활용하기에는 상대적으로 낮은 정확도로 사용에 제한적이다. 이러한 문제를 해결하기 위해 본 논문에서는 대장암의 조기 발견을 위한 비전 트랜스포머 기반 내시경 병리 소견 분류법을 제안한다. 식도염, 폴립, 궤양성 대장염을 포함한 병리학적 소견이 있는 내시경 이미지를 각각 1,000개씩 사용하였으며, 제안된 접근 방식을 사용하여 세 가지 병리학적 소견을 분류할 때 98%의 분류 정확도를 보였다.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• Asian Pacific Journal of Cancer Prevention
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• 제17권7호
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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.

• 대한의용생체공학회:의공학회지
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• 제38권5호
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• pp.271-276
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• 2017

Cancer biomarkers are using in the diagnosis, staging, prognosis and prediction of disease progression. But, there are not sufficiently profiled and validated in early detection and risk classification of prostate cancer. In this study, we have devoted to finding a panel of serum biomarkers that are able to detect the diagnosis of prostate cancer. The serum samples were consisted of 111 prostate cancer and 343 control samples and examined. Eleven biomarkers were constructed in this study, and then nine biomarkers were relevant to candidate biomarkers by using t test. Finally, four biomarkers, PSA, ApoA2, CYFRA21.1 and TTR, were selected as the prostate cancer biomarker panel, logistic regression was used to identify algorithms for diagnostic biomarker combinations(AUC = 0.9697). A panel of combination biomarkers is less invasive and could supplement clinical diagnostic accuracy.