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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Bibliographic Details
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
Subjects
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)
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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