基于浅层与深层特征融合的胃癌前疾病识别

doi:10.3969/j.issn.0258-8021.2020.04.004

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Abstract Precancerous disease recognition is of great significance in reducing the risk of gastric cancer. This paper proposed a method for identifying precancerous diseases based on the fusion of shallow and deep features of gastroscopic images. Firstly,according to properties of gastric images,75-dimensional shallow features were designed manually,including histogram features,texture features and higher order features. Secondly,based on the networks of Resnet and GoogLeNet,we added a fully connected layer before the output layer to extract the deep features of the images. To ensure consistent feature weights,the dimension of the added fully connected layer was 75. Finally,the shallow features were merged with deep features. Machine learning classifiers were used to identify three types of precancerous diseases,namely gastric polyps,gastric ulcers and gastric erosions. We collected 380 images for each disease,and 75% were used as training sets,the others were used as testing sets. We conducted experiments using traditional machine learning,deep learning and feature fusion proposed in this paper. Experiment results showed that the recognition accuracy of the feature fusion method proposed was as high as 95.18%,significantly better than that of traditional machine learning methods (74.12%) and deep learning methods (92.54%). This proposed method made full use of the shallow features and deep features to provide clinical decision support for doctors and assist in the diagnosis of precancerous diseases.

Key words： precancerous diseases feature fusion image recognition machine learning deep learning

Received: 23 July 2019

PACS:

R318

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	Articles by authors
	Pan Yanqi
	Chen Rui
	Zhang Xu
	Zhang Xinsen
	Liu Jiquan
	Hu Weiling
	Duan Huilong
	Si Jianmin

Cite this article:

Pan Yanqi,Chen Rui,Zhang Xu, et al. Precancerous Diseases Classification Based on Fusion of Shallow and Deep Features[J]. Chinese Journal of Biomedical Engineering, 2020, 39(4): 413-421.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2020.04.004 OR http://cjbme.csbme.org/EN/Y2020/V39/I4/413

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