A Lesion Classification Method Using Deep Learning Based on NICE Classification for Computer-Aided Diagnosis System in Colorectal NBI Endoscopy
Currently, video image diagnosis by NBI (Narrow Band Imaging) is commonly used for colonoscopy. The purpose of this paper is to develop a CAD (computer-aided diagnosis) system that can reduce the variability of diagnosis due to differences in clinical doctor's experience by presenting quantitat...
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Published in | 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) pp. 1 - 4 |
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Main Authors | , , , , , , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
27.06.2021
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Subjects | |
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Abstract | Currently, video image diagnosis by NBI (Narrow Band Imaging) is commonly used for colonoscopy. The purpose of this paper is to develop a CAD (computer-aided diagnosis) system that can reduce the variability of diagnosis due to differences in clinical doctor's experience by presenting quantitative inference results to the clinical doctor. As a part of this system development, we create a classifier that classifies lesion images into NICE (NBI International Colorectal Endoscopic) classification using deep learning. We can achieve to develop a CNN (Convolutional Neural Network) based classifier in which five performance indicators (Accuracy, Recall, Specificity, PPV, and NPV) are satisfied more than 90 % quality. |
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AbstractList | Currently, video image diagnosis by NBI (Narrow Band Imaging) is commonly used for colonoscopy. The purpose of this paper is to develop a CAD (computer-aided diagnosis) system that can reduce the variability of diagnosis due to differences in clinical doctor's experience by presenting quantitative inference results to the clinical doctor. As a part of this system development, we create a classifier that classifies lesion images into NICE (NBI International Colorectal Endoscopic) classification using deep learning. We can achieve to develop a CNN (Convolutional Neural Network) based classifier in which five performance indicators (Accuracy, Recall, Specificity, PPV, and NPV) are satisfied more than 90 % quality. |
Author | Tamaki, Toru Katayama, Daisuke Tanaka, Shinji Okamoto, Yuki Yoshida, Shigeto Michida, Ryuichi Izakura, Seiji Koide, Tetsushi Wu, Yongfei Mieno, Hiroshi |
Author_xml | – sequence: 1 givenname: Daisuke surname: Katayama fullname: Katayama, Daisuke email: katayama-daisuke@hiroshima-u.ac.jp organization: Research Institute for Nanodevice and Bio Systems, Hiroshima University – sequence: 2 givenname: Ryuichi surname: Michida fullname: Michida, Ryuichi organization: Research Institute for Nanodevice and Bio Systems, Hiroshima University – sequence: 3 givenname: Seiji surname: Izakura fullname: Izakura, Seiji organization: Research Institute for Nanodevice and Bio Systems, Hiroshima University – sequence: 4 givenname: Yongfei surname: Wu fullname: Wu, Yongfei organization: Research Institute for Nanodevice and Bio Systems, Hiroshima University – sequence: 5 givenname: Tetsushi surname: Koide fullname: Koide, Tetsushi email: koide@hiroshima-u.ac.jp organization: Research Institute for Nanodevice and Bio Systems, Hiroshima University – sequence: 6 givenname: Shinji surname: Tanaka fullname: Tanaka, Shinji organization: Hiroshima University Hospital,Department of Endoscopy – sequence: 7 givenname: Yuki surname: Okamoto fullname: Okamoto, Yuki organization: Hiroshima University Hospital,Department of Endoscopy – sequence: 8 givenname: Hiroshi surname: Mieno fullname: Mieno, Hiroshi organization: Medical Corporation JR Hiroshima Hospital,Department of Gastroenterology – sequence: 9 givenname: Toru surname: Tamaki fullname: Tamaki, Toru organization: Nagoya Institute of Technology,Dept. of Computer Science – sequence: 10 givenname: Shigeto surname: Yoshida fullname: Yoshida, Shigeto organization: Medical Corporation JR Hiroshima Hospital,Department of Gastroenterology |
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Snippet | Currently, video image diagnosis by NBI (Narrow Band Imaging) is commonly used for colonoscopy. The purpose of this paper is to develop a CAD (computer-aided... |
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SubjectTerms | CAD (Computer Aided Diagnosis) System Computers Data preprocessing Deep learning Design automation Endoscopes Imaging Medical services NBI (Narrow Band Imaging) NICE (NBI International Colorectal Endoscopic) |
Title | A Lesion Classification Method Using Deep Learning Based on NICE Classification for Computer-Aided Diagnosis System in Colorectal NBI Endoscopy |
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