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Automatic detection of blood content in capsule endoscopy images based on a deep convolutional neural network
Author(s) -
Aoki Tomonori,
Yamada Atsuo,
Kato Yusuke,
Saito Hiroaki,
Tsuboi Akiyoshi,
Nakada Ayako,
Niikura Ryota,
Fujishiro Mitsuhiro,
Oka Shiro,
Ishihara Soichiro,
Matsuda Tomoki,
Nakahori Masato,
Tanaka Shinji,
Koike Kazuhiko,
Tada Tomohiro
Publication year - 2020
Publication title -
journal of gastroenterology and hepatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.214
H-Index - 130
eISSN - 1440-1746
pISSN - 0815-9319
DOI - 10.1111/jgh.14941
Subject(s) - convolutional neural network , capsule endoscopy , artificial intelligence , medicine , receiver operating characteristic , test set , deep learning , pattern recognition (psychology) , computer science , gastroenterology
Background and Aim Detecting blood content in the gastrointestinal tract is one of the crucial applications of capsule endoscopy (CE). The suspected blood indicator (SBI) is a conventional tool used to automatically tag images depicting possible bleeding in the reading system. We aim to develop a deep learning‐based system to detect blood content in images and compare its performance with that of the SBI. Methods We trained a deep convolutional neural network (CNN) system, using 27 847 CE images (6503 images depicting blood content from 29 patients and 21 344 images of normal mucosa from 12 patients). We assessed its performance by calculating the area under the receiver operating characteristic curve (ROC‐AUC) and its sensitivity, specificity, and accuracy, using an independent test set of 10 208 small‐bowel images (208 images depicting blood content and 10 000 images of normal mucosa). The performance of the CNN was compared with that of the SBI, in individual image analysis, using the same test set. Results The AUC for the detection of blood content was 0.9998. The sensitivity, specificity, and accuracy of the CNN were 96.63%, 99.96%, and 99.89%, respectively, at a cut‐off value of 0.5 for the probability score, which were significantly higher than those of the SBI (76.92%, 99.82%, and 99.35%, respectively). The trained CNN required 250 s to evaluate 10 208 test images. Conclusions We developed and tested the CNN‐based detection system for blood content in CE images. This system has the potential to outperform the SBI system, and the patient‐level analyses on larger studies are required.