An intelligent gastric cancer screening method based on convolutional neural network and support vector machine

被引:0
作者
Wang L. [1 ,2 ]
Yang S. [1 ]
Zhou A. [2 ]
Huang R. [2 ]
Ding S. [1 ]
Wang H. [1 ]
Hu J. [3 ]
机构
[1] School of Management, Hefei University of Technology, Hefei
[2] School of Management Engineering, Anhui Polytechnic University, Wuhu
[3] Department of AI Research, Wuhu Academy of Wanjiang Zone Development, Wuhu
关键词
clinical aided decision-making; convolutional neural network; Intelligent gastric cancer screening; support vector machine;
D O I
10.1080/1206212X.2019.1640345
中图分类号
学科分类号
摘要
The continuous development of modern information technologies such as Internet and cloud computing leads to unprecedented explosive growth of global data volume. The rapid convergence of massive multi-source heterogeneous data provides a driving force for the continuous development of artificial intelligence. The application of big data and artificial intelligence in the medical health field can not only greatly improve efficiency of medical health management services but also effectively promote the renewal and development of medical knowledge and technology. However, intelligent screening of gastric cancer is less concerned in this study. In view of this, this paper proposes an intelligent gastric cancer screening method based on convolutional neural network and support vector machine, and makes empirical analysis of gastroscopy data from the First Affiliated Hospital of Anhui Medical University, further conforming good diagnosis accuracy of this method. © 2019 Informa UK Limited, trading as Taylor & Francis Group.
引用
收藏
页码:720 / 725
页数:5
相关论文
共 18 条
[1]  
Wu J., Zhang L., Yin S., Et al., Differential diagnosis model of hypocellular myelodysplastic syndrome and aplastic anemia based on the medical big data platform, Complexity, 2018, pp. 1-12, (2018)
[2]  
Wu J., Wei W., Zhang L., Et al., Risk assessment of hypertension in steel workers based on LVQ and Fisher-SVM deep excavation, IEEE Access, 7, 1, pp. 23109-23119, (2019)
[3]  
Li W., Jia M., Wang J., Et al., Association of MMP9-1562C/T and MMP13-77A/G polymorphisms with non-small cell lung cancer in southern Chinese population, Biomolecules, 9, 3, (2019)
[4]  
Cooper J.G., West R.M., Clamp S.E., Et al., Does computer-aided clinical decision support improve the management of acute abdominal pain? A systematic review, Emerg Med J, 28, 7, pp. 553-557, (2011)
[5]  
Wright A., Sittig D.E., A four-phase model of the evolution of clinical decision support architectures, Int J Med Inform, 77, 10, pp. 641-643, (2008)
[6]  
Croskerry P., Norman G., Overconfidence in clinical decision making, Am J Med, 121, 5, pp. 24-26, (2008)
[7]  
Prescott J.W., Quantitative imaging biomarkers: the application of advanced image processing and analysis to clinical and preclinical decision making, J Digit Imaging, 26, 1, pp. 97-101, (2013)
[8]  
Kannan S.R., Ramathilagam S., Chung P.C., Effective fuzzy c-means clustering algorithms for data clustering problems, Expert Syst Appl, 39, 7, pp. 6292-6300, (2012)
[9]  
Wang J., Sun X.P., Nahavandi S., Et al., Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis, Comput Meth Prog Bio, 117, 2, pp. 53-56, (2014)
[10]  
Xi H., Zhang K., Wei B., Et al., Significance and contemplation of clinical diagnosis and therapy on the renewal of the eighth edition of gastric cancer TNM staging system, Chinese J Gastrointest Surg, 20, 2, pp. 166-168, (2017)