• International Journal of Computer Science & Network Security
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• v.22 no.5
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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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• v.22 no.4
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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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• v.2 no.1
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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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• v.9 no.2
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1924-1929
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• 2007

Nowadays, a lot of related data obtained from these research could be given a new present meaning to accomplish the original purpose of the whole research as a human project. The method of tumor classification based on microarray could contribute to being accurate tumor classification by finding differently expressing gene pattern statistically according to a tumor type. Therefore, the process to select a closely related informative gene with a particular tumor classification to classify tumor using present microarray technology with effect is essential. In this thesis, we used cDNA microarrays of 3840 genes obtained from neuronal differentiation experiment of cortical stem cells on white mouse with cancer, constructed accurate tumor classification model by extracting informative gene list through normalization separately and then did performance estimation by analyzing and comparing each of the experiment results. Result classifying Multi-Perceptron classifier for selected genes using Pearson correlation coefficient represented the accuracy of 95.6%.

• The Journal of the Korean bone and joint tumor society
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• v.14 no.1
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• pp.1-9
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• 2008

Soft tissue tumor classifications should be an important part of radiology, oncology and, for clinicians and pathologists, they provide diagnostic instruction and prognostic guidelines. In soft tissue tumor classification systems, the World Health Organization (WHO) classifications have become dominant, enabled by the timely publication of new 'blue books' which included detailed text and numerous good illustrations. The new WHO classification of soft tissue tumors was introduced in 2002. Because the classification represents a broad consensus concept, it has gained widespread acceptance around the globe. This article reviews the changes which were introduced the adipose tumors, fibroblastic/myofibroblastic tumors, so-called fibrohistiocytic tumors, smooth muscle tumors, pericytic tumors and skeletal muscle tumors which have been first recognized or properly classified during the past decade.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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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%.

• 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.

• The Journal of the Korean bone and joint tumor society
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• v.14 no.2
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• pp.79-85
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• 2008

Soft tissue tumor classifications should be an important part of radiology, oncology and, for orthopedic clinicians and pathologists, they provide diagnostic instruction and prognostic guidelines. In soft tissue tumor classification systems, the World Health Organization (WHO) classifications have become dominant, enabled by the timely publication of new blue books which included detailed text and numerous good illustrations. The new WHO classification of soft tissue tumors was introduced in 2002. Because the classification represents a broad consensus concept, it has gained widespread acceptance around the globe. This article reviews the changes which were introduced the vascular tumors, chondroid-osseous tumors and tumors of uncertain differentiation which have been first recognized or properly classified during the past decade.

• Biomedical Science Letters
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• v.28 no.4
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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.