Shohei Igarashi1, Yoshihiro Sasaki2, Tatsuya Mikami1, Hirotake Sakuraba1, Shinsaku Fukuda1. 1. Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki, 036-8562, Japan. 2. Department of Medical Informatics, Hirosaki University Hospital, 53 Hon-cho, Hirosaki, 036-8563, Japan. Electronic address: gahiro@hirosaki-u.ac.jp.
Abstract
BACKGROUND: Machine learning has led to several endoscopic studies about the automated localization of digestive lesions and prediction of cancer invasion depth. Training and validation dataset collection are required for a disease in each digestive organ under a similar image capture condition; this is the first step in system development. This data cleansing task in data collection causes a great burden among experienced endoscopists. Thus, this study classified upper gastrointestinal (GI) organ images obtained via routine esophagogastroduodenoscopy (EGD) into precise anatomical categories using AlexNet. METHOD: In total, 85,246 raw upper GI endoscopic images from 441 patients with gastric cancer were collected retrospectively. The images were manually classified into 14 categories: 0) white-light (WL) stomach with indigo carmine (IC); 1) WL esophagus with iodine; 2) narrow-band (NB) esophagus; 3) NB stomach with IC; 4) NB stomach; 5) WL duodenum; 6) WL esophagus; 7) WL stomach; 8) NB oral-pharynx-larynx; 9) WL oral-pharynx-larynx; 10) WL scaling paper; 11) specimens; 12) WL muscle fibers during endoscopic submucosal dissection (ESD); and 13) others. AlexNet is a deep learning framework and was trained using 49,174 datasets and validated using 36,072 independent datasets. RESULTS: The accuracy rates of the training and validation dataset were 0.993 and 0.965, respectively. CONCLUSIONS: A simple anatomical organ classifier using AlexNet was developed and found to be effective in data cleansing task for collection of EGD images. Moreover, it could be useful to both expert and non-expert endoscopists as well as engineers in retrospectively assessing upper GI images.
BACKGROUND: Machine learning has led to several endoscopic studies about the automated localization of digestive lesions and prediction of cancer invasion depth. Training and validation dataset collection are required for a disease in each digestive organ under a similar image capture condition; this is the first step in system development. This data cleansing task in data collection causes a great burden among experienced endoscopists. Thus, this study classified upper gastrointestinal (GI) organ images obtained via routine esophagogastroduodenoscopy (EGD) into precise anatomical categories using AlexNet. METHOD: In total, 85,246 raw upper GI endoscopic images from 441 patients with gastric cancer were collected retrospectively. The images were manually classified into 14 categories: 0) white-light (WL) stomach with indigo carmine (IC); 1) WL esophagus with iodine; 2) narrow-band (NB) esophagus; 3) NB stomach with IC; 4) NB stomach; 5) WL duodenum; 6) WL esophagus; 7) WL stomach; 8) NB oral-pharynx-larynx; 9) WL oral-pharynx-larynx; 10) WL scaling paper; 11) specimens; 12) WL muscle fibers during endoscopic submucosal dissection (ESD); and 13) others. AlexNet is a deep learning framework and was trained using 49,174 datasets and validated using 36,072 independent datasets. RESULTS: The accuracy rates of the training and validation dataset were 0.993 and 0.965, respectively. CONCLUSIONS: A simple anatomical organ classifier using AlexNet was developed and found to be effective in data cleansing task for collection of EGD images. Moreover, it could be useful to both expert and non-expert endoscopists as well as engineers in retrospectively assessing upper GI images.
Authors: J Yogapriya; Venkatesan Chandran; M G Sumithra; P Anitha; P Jenopaul; C Suresh Gnana Dhas Journal: Comput Math Methods Med Date: 2021-09-11 Impact factor: 2.238