• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.143-148
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• 2022

Brain tumor classification is an important process that allows doctors to plan treatment for patients based on the stages of the tumor. To improve classification performance, various CNN-based architectures are used for brain tumor classification. Existing methods for brain tumor segmentation suffer from overfitting and poor efficiency when dealing with large datasets. The enhanced CNN architecture proposed in this study is based on U-Net for brain tumor segmentation, RefineNet for pattern analysis, and SegNet architecture for brain tumor classification. The brain tumor benchmark dataset was used to evaluate the enhanced CNN model's efficiency. Based on the local and context information of the MRI image, the U-Net provides good segmentation. SegNet selects the most important features for classification while also reducing the trainable parameters. In the classification of brain tumors, the enhanced CNN method outperforms the existing methods. The enhanced CNN model has an accuracy of 96.85 percent, while the existing CNN with transfer learning has an accuracy of 94.82 percent.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.101-110
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• 2022

Histopathological analysis of biopsy specimens is still used for diagnosis and classifying the brain tumors today. The available procedures are intrusive, time consuming, and inclined to human error. To overcome these disadvantages, need of implementing a fully automated deep learning-based model to classify brain tumor into multiple classes. The proposed CNN model with an accuracy of 92.98 % for categorizing tumors into five classes such as normal tumor, glioma tumor, meningioma tumor, pituitary tumor, and metastatic tumor. Using the grid search optimization approach, all of the critical hyper parameters of suggested CNN framework were instantly assigned. Alex Net, Inception v3, Res Net -50, VGG -16, and Google - Net are all examples of cutting-edge CNN models that are compared to the suggested CNN model. Using huge, publicly available clinical datasets, satisfactory classification results were produced. Physicians and radiologists can use the suggested CNN model to confirm their first screening for brain tumor Multi-classification.

• Genomics & Informatics
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• 제2권1호
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• pp.19-29
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• 2004

Gene expression profiles may offer more information than morphology and provide an alternative to morphology- based tumor classification systems. Informative gene selection is finding gene subsets that are able to discriminate between tumor types, and may have clear biological interpretation. Gene selection is a fundamental issue in gene expression based tumor classification. In this report, techniques for selecting informative genes are illustrated and supervised shaving introduced as a gene selection method in the place of a clustering algorithm. The supervised shaving method showed good performance in gene selection and classification, even though it is a clustering algorithm. Almost selected genes are related to leukemia disease. The expression profiles of 3051 genes were analyzed in 27 acute lymphoblastic leukemia and 11 myeloid leukemia samples. Through these examples, the supervised shaving method has been shown to produce biologically significant genes of more than $94\%$ accuracy of classification. In this report, SVM has also been shown to be a practicable method for gene expression-based classification.

• Journal of Digestive Cancer Reports
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• 제9권2호
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• pp.57-59
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• 2021

Carcinoid tumor is called as neuroendocrine tumor and is classified into neuroendocrine tumor Grade 1, neuroendocrine tumor Grade 2, and neuroendocrine carcinoma based on the differentiation of tumors. Recently, the incidence of rectal carcinoid tumor has been increasing probably due to the increased interest on screening colonoscopy and the advancement of endoscopic imaging technology. As the rectal carcinoid shows a wide range of clinical characteristics such as metastasis and long-term prognosis depending on the size and histologic features, it is a challenge to give a consistent diagnostic code in patients with the rectal carcinoid. If the rectal carcinoid tumor is less than 1 cm in size, it can be given as the code of definite malignancy or the code of uncertain malignant potential according to International Classification of Diseases for Oncology (ICD-O) by World Health Organization (WHO). Because patients get different amount of benefit from the insurance company based on different diagnostic codes, this inconsistent coding system has caused a significant confusion in the clinical practice. In 2019, WHO updated ICD-O and Statistics Korea subsequently changed Korean Standard Classification of Diseases (KCD) including the code of rectal carcinoid tumors. This review will summarize what has been changed in recent ICD-O and KCD system regarding the rectal carcinoid tumor and surmise its clinical implication.

• 한국정보통신학회논문지
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• 제11권10호
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• pp.1924-1929
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• 2007

오늘날 인간 프로젝트와 같은 종합적 인 연구의 궁극적 목적을 달성하기 위해서는 이 들 연구로부터 획득한 대량의 관련 데이터에 대해 새로운 현실적 의미를 부여할 수 있어야 한다. 마이크로어레이를 기반으로 하는 종양 분류 방법은 종양 종류에 따라 다르게 발현되는 유전자 양상을 통계적으로 발견함으로써 정확한 종양 분류에 기여 할 수 있다. 따라서 현재의 마이크로어레이 기술을 이용해서 효과적으로 종양을 분류하기 위해서는 특정 종양 분류와 밀접하게 관련이 있는 정보력 있는 유전자를 선택하는 과정이 필수적이다. 본 논문에서는 암에 걸린 흰쥐 외피 기간 세포 분화 실험에서 얻어진 3840 유전자의 마이크로어레이 cDNA를 이용해 데이터의 정규화를 거쳐 정보력 있는 유전자 목록을 별도로 추출하여 보다 정확한 종양 분류 모델을 구축하고 각각의 실험 결과들을 비교 분석함으로써 성능평가를 하였다. 피어슨 적률 상관 계수를 이용하여 선택된 유전자들을 멀티퍼셉트론 분류기로 분류한 결과 98.6%의 정확도를 보였다.

• 대한골관절종양학회지
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• 제14권1호
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• pp.1-9
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• 2008

연조직종양의 이해는 과거 10년 동안에 걸쳐 주요 변화와 더불어 실질적인 진보가 있었고, 이를 바탕으로 연조직종양의 새로운 분류가 WHO에 의해 2002년에 이루어졌다. 이 개정은 이전에 발표와 상당히 다른 내용의 접근을 하였고, 이 작업에 유전학과 분자생물학 그리고 임상분야의 전문가들이 참여하였다. 여기에서는 과거에 알고 있었거나 특성이 알려진 많은 종양을 포함하여 새로운 큰 변화나 작은 변화가 일어난 부분에 대해서 정리를 하였다. 이러한 내용을 연조직종양의 새로운 WHO 분류를 중심으로 지방세포종, 섬유모세포성/근육섬유모세포 성종과 소위섬유조직구종, 평활근종, 혈관주위종과 근골격종을 중심으로, 큰 변화와 작은 변화로 나누어서 설명하고 새롭게 소개되는 병명을 소개하고 정리하였다. 이 새로운 WHO의 연조직종양의 분류를 이해하여, 종양의 진단과 예후의 재현을 용이하게 하는 필수적인 지침으로 사용할 수 있을 것으로 생각된다.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.198-204
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• 2021

Glioma is one of the common types of brain tumors starting in the brain's glial cell. These tumors are classified into low-grade or high-grade tumors. Physicians analyze the stages of brain tumors and suggest treatment to the patient. The status of the tumor has an importance in the treatment. Nowadays, computerized systems are used to analyze and classify brain tumors. The accurate grading of the tumor makes sense in the treatment of brain tumors. This paper aims to develop a classification of low-grade glioma and high-grade glioma using a deep learning algorithm. This system utilizes four transfer learning algorithms, i.e., AlexNet, GoogLeNet, ResNet18, and ResNet50, for classification purposes. Among these algorithms, ResNet18 shows the highest classification accuracy of 97.19%.

• 디지털산업정보학회논문지
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• 제14권4호
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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.

• 대한골관절종양학회지
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• 제14권2호
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• pp.79-85
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• 2008

연조직종양의 분류는 종양학에서 영상의학과의사와 임상을 담당하는 정형외과의사, 종양학자 그리고 병리학자의 진단과 예후의 재현을 용이하게 하는 필수적인 지침이다. 연조직종양의 이해는 과거 10년 동안에 걸쳐 주요 변화와 더불어 진보가 있었고, 이를 바탕으로 연조직종양의 새로운 분류가 WHO에 의해 2002년에 이루어졌다. 이 개정은 이전에 발표된 분류와 많은 부분에서 다른 내용의 접근을 하였고, 이 작업에는 유전학과 분자생물학 그리고 임상분야의 전문가들이 참여하였다. 여기에서는 과거에 알고 있었거나 특성이 알려진 종양을 포함하여 새로운 큰 변화나 작은 변화가 일어난 부분에 대해서 정리를 하였다. 이러한 내용의 연조직종양의 새로운 WHO 분류를 혈관종, 연골-골종 그리고 불확실한분화종을 중심으로, 큰 변화와 작은 변화로 나누어서 설명하고 새롭게 소개되는 병명을 정리하였다. 이 새로운 WHO의 연조직 종양의 분류를 이해하여, 종양의 진단과 예후의 재현을 용이하게 하는 필수적인 지침으로 사용할 수 있을 것으로 생각된다.

• 대한의생명과학회지
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• 제28권4호
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• pp.223-228
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• 2022

It has well reported that tumor markers have many advantages like minimally invasive, convenient use, low cost but also has many limitations like low sensitivity and specificity, relevance of prognosis, low organ specificity. Although no tumor markers are ideal, many tumor markers are used for cancer diagnosis, treatment monitoring, and surveillance monitoring after treatment. We review the classification and characteristics of tumor markers according cancer types and clinical roles in current times.