Evidence map›Paper›PMID 34916186›Full record

ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2021

[Screening of early gastric cancer using Pre-Activation Squeeze-and-Excitation ResNet].

X Zhang, Y Wang, J Zhang, H Sun, D Wang, Y Chen, Z Zhou

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Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

X ZhangSchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Y WangSchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
J ZhangSchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
H SunDepartment of Gastroenterology, Changhai Hospital, Shanghai 200433, China.
D WangDepartment of Gastroenterology, Changhai Hospital, Shanghai 200433, China.
Y ChenSeventh Affiliated Hospital of Southern Medical University, Foshan 528244, China.
Z ZhouSchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo propose a quick and accurate method for screening early gastric cancer based on Pre-Activation Squeeze- and-Exception ResNet (PASE-ResNet) gastroscopy images in limited labeled data sets.

methodsWe developed an algorithm based on Pre-Activation Squeeze- and-Exception ResNet for early gastric cancer screening. To focus on the taskrelated image region and enhance the feature expression ability of model, we combined the Squeeze-and-Exception (SE) module with the residual module in PreAct-ResNet to adjust the weight of the feature channel. The strategy of local screening + global sliding window was adopted to improve the performance of early cancer screening. After data expansion, 18 400 set subgraphs were obtained, and the gastroscopy images were examined using the PASE-ResNet model by sliding window.

resultsThe results of experiments showed that the proposed algorithm had good performance for screening early gastric cancer with an accuracy of 98.03%, a sensitivity of 98.96% and a specificity of 96.52%.

conclusionThe PASE-ResNet can achieve a high accuracy for screening early gastric cancer.

Indexed as

Early Detection of CancerStomach NeoplasmsAlgorithmsHumansattention mechanismresidual networkscreening of early gastric cancer

Identifiers

PMID34916186
PMCPMC8685703

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