Smart connected electronic gastroscope system for gastric cancer screening using multi-column convolutional neural networks |
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| Authors: | Hao Wang Desheng Wu Youtao Zhang |
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| Affiliation: | 1. School of Management, Hefei University of Technology, Anhui, China;2. Key Laboratory of Process Optimization and Intelligent Decision-Making (Ministry of Education), Hefei University of Technology, Anhui, China;3. Economics and Management School, University of Chinese Academy of Sciences, Beijing, PR China;4. Computer Science Department, University of Pittsburgh, Pittsburgh, PA, USA |
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| 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. |
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| Keywords: | artificial intelligence decision support systems complex product endoscopy system gastric cancer screening convolutional neural network |
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