Research on Virus Morphology Recognition Method Based on Enhanced Graph Convolutional Network
Background Transmission electron microscope (TEM) is an important method to detect virus. TEM detection often relies on manual observation by experts, and the operation steps are cumbersome. Moreover, existing machine learning methods are easily affected by background and noise, resulting in poor vi...
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Veröffentlicht in: | Zhongguo quanke yixue 2022-05, Vol.25 (14), p.1749-1756 |
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Sprache: | chi |
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Zusammenfassung: | Background Transmission electron microscope (TEM) is an important method to detect virus. TEM detection often relies on manual observation by experts, and the operation steps are cumbersome. Moreover, existing machine learning methods are easily affected by background and noise, resulting in poor virus detection methods, low efficiencyand time consuming. Objective In order to improve the efficiency of TEM virus detection, an Enhanced Graph Convolution Network (EGCN) is proposed to solve the problem of automatic identification of virus morphology in TEM images. Methods In this model, Convolutional Neural Network (CNN) was used to extract the local feature information between pixels, and GCN was used for graph feature learning combined with the nearest neighbor relationship between sample features. In the model optimization, the group super classification loss and classification cross entropy loss were introduced to improve the feature extraction ability of the model for virus category information, and further |
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ISSN: | 1007-9572 |
DOI: | 10.12114/j.issn.1007-9572.2022.0123 |