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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2021.6
- 수록면
- 196 - 196 (1page)
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이 논문의 연구 히스토리 (5)
2021
Machine Learning Identification of Colorectal Cancer Stage Based on Recurrent Neural Network Model Algorithm
Rifaldy Faja
,
Dewi Mustika
대한외과학회 학술대회 초록집
2021.11
학술대회자료
Machine Learning Classification of Breast Cancer Stage Based on Fuzzy Neural Network Model Algorithm
Rifaldy Faja
대한외과학회 학술대회 초록집
2021.11
학술대회자료
Machine Learning Classification of Colorectal Cancer Stage Using Recurrent Neural Network (RNN) Model Algorithm
Rifaldy Faja
,
Nana Ind
,
i
대한임상종양학회 학술대회지
2021.06
학술대회자료
2020
Classification of Colorectal Cancer Stadium Using Recurrent Neural Network (RNN) Model Algorithm
Rifaldy Faja
,
P
,
ihantini Jup
외 4명
대한외과학회 학술대회 초록집
2020.11
학술대회자료
Classification of Breast Cancer Stadium Using Fuzzy Neural Network (FNN) Model Algorithm
Rifaldy Faja
,
P
,
ihantini Jup
외 2명
대한외과학회 학술대회 초록집
2020.11
학술대회자료
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Background/Aims
Colorectal cancer or also known as colon cancer is a malignant tumor found in the colon or rectum. One way to detect colorectal cancer is by using colonoscopy. Once colorectal cancer is detected, a classification is performed to determine the stage of cancer. In this study, the Recurrent Neural Network (RNN) model was used for the classification of colorectal cancer stages. This study aims to explain the procedure and the accuracy of Elman tissue RNN modeling in the classification of colorectal cancer stages from colonoscopy photographs.
Methods
The process carried out is to convert the image of red green blue (RGB) to a grayscale image on the colonoscopy data. After that, the image was extracted with Gray Level Co-occurrence Matrix which was designed using Graphical User Interface (GUI) with Matlab. There are 14 features, namely energy, contrast, correl ... 전체 초록 보기
Colorectal cancer or also known as colon cancer is a malignant tumor found in the colon or rectum. One way to detect colorectal cancer is by using colonoscopy. Once colorectal cancer is detected, a classification is performed to determine the stage of cancer. In this study, the Recurrent Neural Network (RNN) model was used for the classification of colorectal cancer stages. This study aims to explain the procedure and the accuracy of Elman tissue RNN modeling in the classification of colorectal cancer stages from colonoscopy photographs.
Methods
The process carried out is to convert the image of red green blue (RGB) to a grayscale image on the colonoscopy data. After that, the image was extracted with Gray Level Co-occurrence Matrix which was designed using Graphical User Interface (GUI) with Matlab. There are 14 features, namely energy, contrast, correl ... 전체 초록 보기
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UCI(KEPA) : I410-ECN-0101-2021-514-001919672