Research on CT image classification algorithm of COVID-19 based on improved ResNet
2022 International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology, AIoTC 2022
; 3351:46-51, 2022.
Article
in English
| Scopus | ID: covidwho-2254659
ABSTRACT
The classification of COVID-19 and other viral pneumonias will help doctors to diagnose new coronary patients more accurately and quickly. Aiming at the classification problem of CT in patients with COVID-19, this paper proposes a CT image classification method based on an improved ResNet50 network based on the traditional convolutional neural network classification model. This paper uses the multiscale feature fusion strategy, combined with the improved attention mechanism to obtain the correlation coefficient between the internal feature points of the feature map, and finally achieves the effect of enhancing the representation ability of the feature map. Through the analysis and comparison of the technical principle, classification accuracy, and other parameters, it shows that the improved algorithm has better adaptive ability and classification ability. Through experiments, the improved ResNet50 classification model has a certain improvement in accuracy, time complexity, and spatial complexity compared with the traditional classification model, and the accuracy rate can reach 90.1 %. © 2022 Copyright for this paper by its authors.
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2022 International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology, AIoTC 2022
Year:
2022
Document Type:
Article
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