Smart connected electronic gastroscope system for gastric cancer screening using multi-column convolutional neural networks

Abstract

Gastroscopy is a widely adopted method for gastric cancer screening and early diagnosis. Clinical studies show that it can effectively prolong patient life and maximise therapeutic effect. However, it is difficult for doctors to identify and detect lesions in real time, which manifests as the major challenge in gastroscopy. In this paper, we propose SCEG, a smart connected electronic gastroscopy system that performs dynamic cancer screening in gastroscopy. By integrating electronic gastroscopy with cloud-based medical image analysis service, we develop an AdaBoost-based multi-column convolutional neural network (MCNN) for enhancing gastric cancer screening. Experimental results show that the proposed MCNN approach significantly outperforms other competing approaches.

Keywords:

• artificial intelligence
• decision support systems
• complex product
• endoscopy system
• gastric cancer screening
• convolutional neural network

Additional information

Funding

This work was supported in part by National Nature Science Foundation of China [grant number 71690235], [grant number 71571058], [grant number 91546102]; Anhui Provincial Science and Technology Major Project [grant number 16030801121], [grant number 17030801001].