Classification and detection of COVID-19 X-Ray images based on DenseNet and VGG16 feature fusion.
- Authors
- Type
- Published Article
- Journal
- Biomedical Signal Processing and Control
- Publisher
- Elsevier
- Publication Date
- Aug 01, 2022
- Volume
- 77
- Pages
- 103772–103772
- Identifiers
- DOI: 10.1016/j.bspc.2022.103772
- PMID: 35573817
- Source
- Medline
- Keywords
- Language
- English
- License
- Unknown
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. © 2022 Elsevier Ltd. All rights reserved.
