Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images
- 1.2k Downloads
Image recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images.
A CNN-based diagnostic system was constructed based on Single Shot MultiBox Detector architecture and trained using 13,584 endoscopic images of gastric cancer. To evaluate the diagnostic accuracy, an independent test set of 2296 stomach images collected from 69 consecutive patients with 77 gastric cancer lesions was applied to the constructed CNN.
The CNN required 47 s to analyze 2296 test images. The CNN correctly diagnosed 71 of 77 gastric cancer lesions with an overall sensitivity of 92.2%, and 161 non-cancerous lesions were detected as gastric cancer, resulting in a positive predictive value of 30.6%. Seventy of the 71 lesions (98.6%) with a diameter of 6 mm or more as well as all invasive cancers were correctly detected. All missed lesions were superficially depressed and differentiated-type intramucosal cancers that were difficult to distinguish from gastritis even for experienced endoscopists. Nearly half of the false-positive lesions were gastritis with changes in color tone or an irregular mucosal surface.
The constructed CNN system for detecting gastric cancer could process numerous stored endoscopic images in a very short time with a clinically relevant diagnostic ability. It may be well applicable to daily clinical practice to reduce the burden of endoscopists.
KeywordsStomach neoplasms Neural networks (computer) Artificial intelligence Endoscopy
The authors thank Yuma Endo and other engineers at AI Medical Service, Inc. (Tokyo, Japan), for their cooperation in developing the CNN.
Study concept and design (TH, KA, TT, SI and TT), acquisition of data (TH, SS, TO, TO, KM and TT), analysis and interpretation of data (TH, KA, TT, SI and TT), drafting of the manuscript (TH, KA, TT, SI, SS, TO, MF, JF and TT)
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1964 and later versions. Informed consent or substitute for it was obtained from all patients for being included in the study.
- 1.GLOBOCAN 2012. Available from: http://globocan.iarc.fr/Pages/fact_sheets_cancer.aspx on 28 April 2017.
- 3.Katai H, Ishikawa T, Akazawa K, Isobe Y, Miyashiro I, Oda I, et al. Five-year survival analysis of surgically resected gastric cancer cases in Japan: a retrospective analysis of more than 100,000 patients from the nationwide registry of the Japanese Gastric Cancer Association (2001–2007). Gastric Cancer. 2017. https://doi.org/10.1007/s10120-017-0716-7 (Epub ahead of print).Google Scholar
- 8.Jeon HK, Kim GH, Lee BE, Park DY, Song GA, Kim DH, et al. Long-term outcome of endoscopic submucosal dissection is comparable to that of surgery for early gastric cancer: a propensity-matched analysis. Gastric Cancer. 2017. https://doi.org/10.1007/s10120-017-0719-4 (Epub ahead of print).Google Scholar
- 27.Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Proceeding NIPS'12 Proceedings of the 25th International Conference on Neural Information Processing Systems, vol. 1. Lake Tahoe, Nevada; 2012. pp. 1097–105. https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
- 28.Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015:1–9.Google Scholar
- 29.Deng, J. Dong W, Socher R, Li L, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: EEE Conference on Computer Vision and Pattern Recognition. 2009:248–55.Google Scholar