Segmentation on Chest CT Imaging in COVID- 19 Based on the Improvement Attention U-Net Model
21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022
; 355:596-606, 2022.
Article
in English
| Scopus | ID: covidwho-2089732
ABSTRACT
This paper proposes a new deep learning model to detect COVID-19 lesions in chest CT images. This method is based on the Attention U-net which uses the layer of Atrous Spatial Pyramid Pooling (ASPP) to capture the feature on various scales. It also contains an attention gate. The attention gate provides the ability to suppress irrelevant regions and focus on the useful feature in an input image. The experimental results show that this method can achieve 99.61% accuracy and 80.43% precision. They are more effectively than the baseline method on Chest CT images. © 2022 The authors and IOS Press. All rights reserved.
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Database:
Scopus
Language:
English
Journal:
21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022
Year:
2022
Document Type:
Article
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