Classification and detection of COVID-19 X-Ray images based on DenseNet and VGG16 feature fusion.
Biomed Signal Process Control
; 77: 103772, 2022 Aug.
Article
in En
| MEDLINE
| ID: mdl-35573817
ABSTRACT
Since December 2019, the novel coronavirus disease (COVID-19) caused by the syndrome coronavirus 2 (SARS-CoV-2) strain has spread widely around the world and has become a serious global public health problem. For this high-speed infectious disease, the application of X-ray to chest diagnosis plays a key role. In this study, we propose a chest X-ray image classification method based on feature fusion of a dense convolutional network (DenseNet) and a visual geometry group network (VGG16). This paper adds an attention mechanism (global attention machine block and category attention block) to the model to extract deep features. A residual network (ResNet) is used to segment effective image information to quickly achieve accurate classification. The average accuracy of our model in detecting binary classification can reach 98.0%. The average accuracy for three category classification can reach 97.3%. The experimental results show that the proposed model has good results in this work. Therefore, the use of deep learning and feature fusion technology in the classification of chest X-ray images can become an auxiliary tool for clinicians and radiologists.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Diagnostic_studies
Language:
En
Journal:
Biomed Signal Process Control
Year:
2022
Document type:
Article
Affiliation country: