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Endoscopy report mining for intelligent gastric cancer screening
journal contribution
posted on 2020-06-01, 00:00 authored by Jinxin Pan, Shuai Ding, Shanlin Yang, Gang LiGang Li, Xiao LiuXiao LiuEndoscopy is an important tool for gastric cancer screening. Due to the lack of effective decision support system for endoscopy, the detection of gastric cancer in the clinic is usually with low sensitivity. In this paper, we propose a Genetic Algorithm optimized Neural Network (GAoNN) approach for gastric cancer detection based on endoscopy reports mining. Considering the fact that gastric cancer sensitivity can significantly improve the 5‐year survival rate of patients, both the prediction accuracy and the sensitivity are employed to construct a multiobjective optimization model for enhancing the classification performance of GAoNN. In particular, we extended an effective genetic algorithm Nondominated Sorting Genetic Algorithm II (NSGA‐II) to train a neural network and reduced the complexity in training hyperparameters and improved the efficiency by substituting the computationally intensive stochastic gradient descent (SGD) algorithm in a neural network. Specifically, we designed the novel crossover and mutation operators and modified the nondominated ranking and crowding distance sorting procedures in NSGA‐II for GAoNN. Through testing on 8,546 real‐world endoscopy reports, we show that GAoNN achieves a prediction accuracy up to 83.74%, which is better than several competitors by significantly increasing sensitivity to 83.14%. GAoNN also reduces the training time by 30.94% when compared with conventional SGD‐based training, which indicates the feasibility of GAoNN in clinical practice.
History
Journal
Expert systemsVolume
37Issue
3Season
Special Issue: New data envelopment analysis models for assessing sustainability: Part 1: A dynamic data envelopment analysis approachArticle number
e12504Pagination
1 - 14Publisher
WileyLocation
Chichester, Eng.Publisher DOI
ISSN
0266-4720eISSN
1468-0394Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
Keywords
Science & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Theory & MethodsComputer Sciencegastric cancer screeninghyperparametersmultiobjective optimizationneural networksNSGA-IIGENETIC ALGORITHM APPROACHDECISION-SUPPORT-SYSTEMNEURAL-NETWORKOPTIMIZATIONDIAGNOSISArtificial Intelligence and Image Processing
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