A Lesion Classification Method Using Deep Learning Based on NICE Classification for Computer-Aided Diagnosis System in Colorectal NBI Endoscopy

• Published in: 2021 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
• Language: English
• 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)
• DOI: 10.1109/ITC-CSCC52171.2021.9501264

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.