| Issue |
ITM Web Conf.
Volume 45, 2022
2021 3rd International Conference on Computer Science Communication and Network Security (CSCNS2021)
|
|
|---|---|---|
| Article Number | 01035 | |
| Number of page(s) | 7 | |
| Section | Computer Technology and System Design | |
| DOI | https://doi.org/10.1051/itmconf/20224501035 | |
| Published online | 19 May 2022 | |
Design and implementation of deep learning algorithm for image discrimination of light field confocal endoscopy for esophageal cancer and gastric cancer
1
Technology and Culture Department, Beijing Computer Center, 100094, Beijing, China
2
Gastroenterology Department, Peking University Third Hospital, 100191, Beijing, China
3
Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100083, Beijing, China
* Corresponding author: tangyy@bcc.ac.cn
The survival rate of early gastric cancer and esophageal cancer is more than 90%. Confocal endoscopy can detect cell morphology and mucosal glandular structure (depth about 500 μm), presenting a cross-sectional microscopic image unfamiliar to both endoscopists and pathologists. Therefore, using computer-aided diagnosis technology to complete real-time artificial intelligence diagnosis of early esophageal and gastric cancer is of great significance for early detection and early treatment of cancer patients. ResNet convolution neural network based image classification model, the introduction of attention mechanism based on CBAM module to improve the performance of the model, to achieve intelligent diagnosis of confocal endoscopy images.
© The Authors, published by EDP Sciences, 2022
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