UM
Intelligent diagnosis of gastric intestinal metaplasia based on convolutional neural network and limited number of endoscopic images
Yan,Tao1,2; Wong,Pak Kin2; Choi,I. Cheong3; Vong,Chi Man4; Yu,Hon Ho3
2020-11-01
Source PublicationComputers in Biology and Medicine
ISSN0010-4825
Volume126
AbstractBackground: Gastric intestinal metaplasia (GIM) is a precancerous lesion of gastric cancer. Currently, diagnosis of GIM is based on the experience of a physician, which is liable to interobserver variability. Thus, an intelligent diagnostic (ID) system, based on narrow-band and magnifying narrow-band images, was constructed to provide objective assistance in the diagnosis of GIM. Method: We retrospectively collected 1880 endoscopic images (1048 GIM and 832 non-GIM) via biopsy from 336 patients confirmed histologically as GIM or non-GIM, from the Kiang Wu Hospital, Macau. We developed an ID system with these images using a modified convolutional neural network algorithm. A separate test dataset containing 477 pathologically confirmed images (242 GIM and 235 non-GIM) from 80 patients was used to test the performance of the ID system. Experienced endoscopists also examined the same test dataset, for comparison with the ID system. One of the challenges faced in this study was that it was difficult to obtain a large number of training images. Thus, data augmentation and transfer learning were applied together. Results: The area under the receiver operating characteristic curve was 0.928 for the pre-patient analysis of the ID system, while the sensitivities, specificities, and accuracies of the ID system against those of the human experts were (91.9% vs. 86.5%, p-value = 1.000) (86.0% vs. 81.4%, p-value = 0.754), and (88.8% vs. 83.8%, p-value = 0.424), respectively. Even though the three indices of the ID system were slightly higher than those of the human experts, there were no significant differences. Conclusions: In this pilot study, a novel ID system was developed to diagnose GIM. This system exhibits promising diagnostic performance. It is believed that the proposed system has the potential for clinical application in the future.
KeywordConvolutional neural network Gastric intestinal metaplasia Intelligent diagnosis Magnifying narrow-band imaging Narrow-band imaging
DOI10.1016/j.compbiomed.2020.104026
URLView the original
Language英语
Scopus ID2-s2.0-85092506052
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorWong,Pak Kin
Affiliation1.School of Mechanical Engineering,Hubei University of Arts and Science,Xiangyang,441053,China
2.Department of Electromechanical Engineering,University of Macau,Taipa,Macau,999078,China
3.Kiang Wu Hospital,Macau,China
4.Department of Computer and Information Science,University of Macau,Taipa,Macau,999078,China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Yan,Tao,Wong,Pak Kin,Choi,I. Cheong,et al. Intelligent diagnosis of gastric intestinal metaplasia based on convolutional neural network and limited number of endoscopic images[J]. Computers in Biology and Medicine,2020,126.
APA Yan,Tao,Wong,Pak Kin,Choi,I. Cheong,Vong,Chi Man,&Yu,Hon Ho.(2020).Intelligent diagnosis of gastric intestinal metaplasia based on convolutional neural network and limited number of endoscopic images.Computers in Biology and Medicine,126.
MLA Yan,Tao,et al."Intelligent diagnosis of gastric intestinal metaplasia based on convolutional neural network and limited number of endoscopic images".Computers in Biology and Medicine 126(2020).
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