2019
DOI: 10.1055/a-0981-6133
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Automated classification of gastric neoplasms in endoscopic images using a convolutional neural network

Abstract: Background Visual inspection, lesion detection, and differentiation between malignant and benign features are key aspects of an endoscopist’s role. The use of machine learning for the recognition and differentiation of images has been increasingly adopted in clinical practice. This study aimed to establish convolutional neural network (CNN) models to automatically classify gastric neoplasms based on endoscopic images. Methods Endoscopic white-light images of pathologically confirmed gastric lesions w… Show more

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Cited by 110 publications
(110 citation statements)
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“…Consecutive patients who were found to have any type of gastric neoplasm during upper gastrointestinal endoscopy between 2010 and 2017 at Chuncheon Sacred Heart Hospital were enrolled. The aim of endoscopic examinations and detailed procedures are described in our previous report [19]. All the neoplasm-suspected lesions that were resected using either the endoscopic resection (endoscopic mucosal resection or endoscopic submucosal dissection technique (ESD)) or surgical resection were included.…”
Section: Collection Of Datamentioning
confidence: 99%
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“…Consecutive patients who were found to have any type of gastric neoplasm during upper gastrointestinal endoscopy between 2010 and 2017 at Chuncheon Sacred Heart Hospital were enrolled. The aim of endoscopic examinations and detailed procedures are described in our previous report [19]. All the neoplasm-suspected lesions that were resected using either the endoscopic resection (endoscopic mucosal resection or endoscopic submucosal dissection technique (ESD)) or surgical resection were included.…”
Section: Collection Of Datamentioning
confidence: 99%
“…Pathological assessment of each lesion was carried out by two pathologists. Samples defined as tumors were cross-checked by yet another pathologist in the Chuncheon Sacred Heart hospital [19].…”
Section: Collection Of Datamentioning
confidence: 99%
See 1 more Smart Citation
“…Tumour heterogeneity between individuals and the difficulty of finding large databases of annotated medical images is a challenge for researchers. However, most studies addressed progress analysis of developing gastric cancer, such as classification and detection of gastritis, [34][35][36][37] Helicobacter pylori, [38][39][40] gastric neoplasms 41 and gastric ulcers 42 43 Two of the papers were restricted reviews regarding only images of endoscopy and addressed the effects of artificial intelligence on gastroenterology, and both were not included in the final database. Also, both reviews addressed challenges in the development of computeraided diagnostic systems.…”
Section: Publication Timelinementioning
confidence: 99%
“…and colonoscopy images have become a vigorous research field, and these systems have demonstrated promising performance in the field of gastroenteroloy. [7][8][9] Typically, a CE video includes an average number of 50,000-60,000 frames in a single examination, requiring an average of 30-120 min of reading time by physicians, depending on the experience level of the reader. 5,10 Because physicians passively read numerous images with intense focus and attention, CE reading is a time-consuming and tedious process.…”
Section: Introductionmentioning
confidence: 99%