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 in2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) pp. 1 - 4
Main Authors Katayama, Daisuke, Michida, Ryuichi, Izakura, Seiji, Wu, Yongfei, Koide, Tetsushi, Tanaka, Shinji, Okamoto, Yuki, Mieno, Hiroshi, Tamaki, Toru, Yoshida, Shigeto
Format Conference Proceeding
LanguageEnglish
Published IEEE 27.06.2021
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Summary: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.
DOI:10.1109/ITC-CSCC52171.2021.9501264